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Electronics
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Intelligent Systems in Industry 4.0
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Intelligent Systems in Industry 4.0

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 770

Special Issue Editors

Dr. Weikai Ren

E-Mail Website
Guest Editor
Institute of Energy, Peking University, Beijing, 100871, China
Interests: multiphase flow measurement; sensor system design; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Siyuan Chang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: Model Predictive Control; Embedded Computing; Deep Learning; Neural Network
Dr. Qian Jiang

E-Mail Website
Guest Editor
School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: power system; smart grid; integrated energy systems

Special Issue Information

Dear Colleagues,

The concept of Industry 4.0 has recently expanded the possibilities for advanced technologies in intelligent systems. Energy systems are undergoing a revolutionary transformation through the integration of highly automated and intelligent technologies to achieve more efficient, sustainable, and economical energy management. These changes not only cover the transformation and upgrading of traditional energy systems, but also include the optimization and control of emerging clean energy systems.

The focus of this Special Issue lies in the recent advancements pertaining to intelligent systems, measurement technology, modeling and optimization of energy systems, and low-carbon technology, as well as the applications of artificial intelligence, Internet of Things, and cyber–physical systems to cater to the requirements posed by Industry 4.0.

Dr. Weikai Ren
Dr. Siyuan Chang
Dr. Qian Jiang
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

  • artificial intelligence
  • Industry 4.0
  • energy systems optimization
  • measurement technology
  • Internet of Things
  • industrial Internet of Things
  • cyber–physical systems
  • advanced image processing in industry
  • process optimization
  • carbon capture usage and storage
  • renewable energy system

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Published Papers (2 papers)

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Research

20 pages, 5808 KiB  
Article
Enhanced YOLOv7 Based on Channel Attention Mechanism for Nearshore Ship Detection
by Qingyun Zhu, Zhen Zhang and Ruizhe Mu
Electronics 2025, 14(9), 1739; https://doi.org/10.3390/electronics14091739 - 24 Apr 2025
Viewed by 294
Abstract
Nearshore ship detection is an important task in marine monitoring, playing a significant role in navigation safety and controlling illegal smuggling. The continuous research and development of Synthetic Aperture Radar (SAR) technology is not only of great importance in military and maritime security [...] Read more.
Nearshore ship detection is an important task in marine monitoring, playing a significant role in navigation safety and controlling illegal smuggling. The continuous research and development of Synthetic Aperture Radar (SAR) technology is not only of great importance in military and maritime security fields but also has great potential in civilian fields, such as disaster emergency response, marine resource monitoring, and environmental protection. Due to the limited sample size of nearshore ship datasets, it is difficult to meet the demand for the large quantity of training data required by existing deep learning algorithms, which limits the recognition accuracy. At the same time, artificial environmental features such as buildings can cause significant interference to SAR imaging, making it more difficult to distinguish ships from the background. Ship target images are greatly affected by speckle noise, posing additional challenges to data-driven recognition methods. Therefore, we utilized a Concurrent Single-Image GAN (ConSinGAN) to generate high-quality synthetic samples for re-labeling and fused them with the dataset extracted from the SAR-Ship dataset for nearshore image extraction and dataset division. Experimental analysis showed that the ship recognition model trained with augmented images had an accuracy increase of 4.66%, a recall rate increase of 3.68%, and an average precision (AP) with Intersection over Union (IoU) at 0.5 increased by 3.24%. Subsequently, an enhanced YOLOv7 algorithm (YOLOv7 + ESE) incorporating channel-wise information fusion was developed based on the YOLOv7 architecture integrated with the Squeeze-and-Excitation (SE) channel attention mechanism. Through comparative experiments, the analytical results demonstrated that the proposed algorithm achieved performance improvements of 0.36% in precision, 0.52% in recall, and 0.65% in average precision (AP@0.5) compared to the baseline model. This optimized architecture enables accurate detection of nearshore ship targets in SAR imagery. Full article
(This article belongs to the Special Issue Intelligent Systems in Industry 4.0)
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26 pages, 5162 KiB  
Article
An Industry 4.0 Solution for Business Games Applied to Museum Context and Learning Experiences
by Gerardo Iovane, Iana Fominska, Marta Chinnici and Nataliia Zamkova
Electronics 2025, 14(7), 1240; https://doi.org/10.3390/electronics14071240 - 21 Mar 2025
Viewed by 279
Abstract
In the context of managing museums, historical, artistic, and archaeological heritage, an advanced decision support system (DSS) can serve as the engine for a business game platform, optimizing decision paths and management strategies. In complex, multi-parameter scenarios, the final decision is often only [...] Read more.
In the context of managing museums, historical, artistic, and archaeological heritage, an advanced decision support system (DSS) can serve as the engine for a business game platform, optimizing decision paths and management strategies. In complex, multi-parameter scenarios, the final decision is often only part of the process; it is equally essential to follow the decision-making path, that is, the sequence of actions necessary to reach the objective. The DSS presented here simplifies the problem by transforming the initial n-dimensional space, defined by the critical success factors (CSFs) selected by experts, into a two-dimensional space. Indeed, thanks to this approach, the computational complexity is reduced to the point that the technological solution can be used even on standard desktop computers and not only on high-performance computing systems. Moreover, the user does not necessarily need to be an IT expert but rather a specialist in the cultural domain. Through grid-based motion algorithms and a hierarchy of CSF priorities, the system quickly identifies optimal solutions in the 2D plane and then maps them back to the n-dimensional space to maintain consistency with the original context. Since the correspondence between n-dimensional micro-states and two-dimensional macro-states is not one-to-one, the DSS returns the specific micro-state of interest from the optimal macro-state, selecting the most effective path. This research aims to develop algorithms that by minimizing entropy and optimizing the system’s dynamics, build optimal paths in the 2D plane, with algorithms capable of restoring the solution in the initial space. Several use cases in the form of business games have been conducted, demonstrating the value of the proposed solution. The result of this work is a simulation environment useful for museum experts to analyze the impact of their management strategies. Thanks to the ability to assign weights to each of the critical success factors (CSFs), the system can display both qualitative and quantitative simulations of museum dynamics as the weights associated with different CSFs vary. Given the system’s generality, it is applicable to various fields where complex business games are required, such as cultural heritage management, logistics, transportation, healthcare systems, and, more broadly, any context where strategic business analysis is needed for the economic enhancement of resources and their optimization. Full article
(This article belongs to the Special Issue Intelligent Systems in Industry 4.0)
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