Electronics

19 pages, 9611 KiB

Open AccessArticle

An Improved Collaborative Control Scheme to Resist Grid Voltage Unbalance for BDFG-Based Wind Turbine

by Defu Cai, Rusi Chen, Sheng Hu, Guanqun Sun, Erxi Wang and Jinrui Tang

Electronics 2024, 13(17), 3582; https://doi.org/10.3390/electronics13173582 - 9 Sep 2024

Viewed by 722

This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during [...] Read more.

This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during unbalanced grid voltage condition is established. On this base, the improved collaborative control between MSC and GSC is presented. Under the control, the control objective of the whole BDFGWT system, including canceling the pulsations of electromagnetic torque and the unbalance of BDFGWT’s total currents, pulsations of BDFGWT’s total powers are capable of being realized; therefore, the control capability of BDFGWT to resist unbalanced grid voltage is greatly improved. Moreover, improved single-loop current controllers adopting PR regulators are proposed for both MSC and GSC where the sequence extractions for both MSC and GSC currents are not needed any more, and hence the proposed control is much simpler. In addition, the transient characteristics are also improved. Moreover, in order to achieve the decoupling control of current and average power, current controller also adopts the feedforward control approach. Case studies for a two MW BDFGWT system are implemented, and the results verify that the presented control is capable of effectively improving the control capability for BDFGWT to resist grid voltage unbalance and exhibit good stable and dynamic control performances. Full article

(This article belongs to the Special Issue Advances in Renewable Energy and Electricity Generation)

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12 pages, 3583 KiB

Open AccessArticle

Smart Transfer Planer with Multiple Antenna Arrays to Enhance Low Earth Orbit Satellite Communication Ground Links

by Mon-Li Chang, Ding-Bing Lin, Hui-Tzu Rao, Hsuan-Yu Lin and Hsi-Tseng Chou

Electronics 2024, 13(17), 3581; https://doi.org/10.3390/electronics13173581 - 9 Sep 2024

Viewed by 624

Abstract

In this study, we propose a smart transfer planer equipped with multiple antenna arrays to improve ground links for low Earth orbit (LEO) satellite communication. The STP features a symmetrical structure and is strategically placed on both ends of a window, serving both [...] Read more.

In this study, we propose a smart transfer planer equipped with multiple antenna arrays to improve ground links for low Earth orbit (LEO) satellite communication. The STP features a symmetrical structure and is strategically placed on both ends of a window, serving both indoor and outdoor environments. Using the window glass as a medium, energy transmission occurs through a coupling mechanism between the planers. The design focuses on large array antenna design, beamforming networks, and coupler design on both sides of the glass. Beamforming networks enable the indoor and outdoor antenna arrays to switch beams in various directions, optimizing high-gain antennas with narrow beamwidths. Through electromagnetic induction and filter couplers, a robust signal transmission channel is established between indoor and outdoor environments. This setup significantly enhances communication efficiency, particularly in non-line-of-sight environments. Full article

(This article belongs to the Special Issue Research and Applications of Antenna Miniaturization, Broadband and Multipolarization Technologies)

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15 pages, 4200 KiB

Open AccessArticle

Research on Rail Surface Defect Detection Based on Improved CenterNet

by Yizhou Mao, Shubin Zheng, Liming Li, Renjie Shi and Xiaoxue An

Electronics 2024, 13(17), 3580; https://doi.org/10.3390/electronics13173580 - 9 Sep 2024

Viewed by 870

Abstract

Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. [...] Read more.

Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. We replace ResNet with ResNeXt and implement a multi-branch structure for better low-level feature extraction. Additionally, we integrate SKNet attention mechanism with the C2f structure from YOLOv8, improving the model’s focus on critical image regions and enhancing the detection of minor defects. We also introduce an elliptical Gaussian kernel for size regression loss, better representing the aspect ratio of rail defects. This approach enhances detection accuracy and speeds up training. Our model achieves a mean accuracy (mAP) of 0.952 on the rail defects dataset, outperforming other models with a 6.6% improvement over the original and a 35.5% increase in training speed. These results demonstrate the efficiency and reliability of our method for rail defect detection. Full article

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26 pages, 10477 KiB

Open AccessArticle

Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization

by Dongbao Jia, Ming Cao, Jing Sun, Feimeng Wang, Wei Xu and Yichen Wang

Electronics 2024, 13(17), 3579; https://doi.org/10.3390/electronics13173579 - 9 Sep 2024

Cited by 1 | Viewed by 680

Abstract

Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable [...] Read more.

Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty. Full article

(This article belongs to the Special Issue Energy Sources Integrated with Power Distribution Systems Using Machine Learning Approach)

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48 pages, 11785 KiB

Open AccessReview

State-of-the-Art Electric Vehicle Modeling: Architectures, Control, and Regulations

by Hossam M. Hussein, Ahmed M. Ibrahim, Rawan A. Taha, S. M. Sajjad Hossain Rafin, Mahmoud S. Abdelrahman, Ibtissam Kharchouf and Osama A. Mohammed

Electronics 2024, 13(17), 3578; https://doi.org/10.3390/electronics13173578 - 9 Sep 2024

Viewed by 1611

Abstract

The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO₂ emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), [...] Read more.

The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO₂ emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), promoting mobility while reducing dependence on fossil fuels. However, this trend is accompanied by multiple challenges related to EVs’ traction systems, storage capacity, chemistry, charging infrastructure, and techniques. Additionally, the requisite energy management technologies and the standards and regulations needed to facilitate the expansion of the EV market present further complexities. This paper provides a comprehensive and up-to-date review of the state of the art concerning EV-related components, including energy storage systems, electric motors, charging topologies, and control techniques. Furthermore, the paper explores each sector’s commonly used standards and codes. Through this extensive review, the paper aims to advance knowledge in the field and support the ongoing development and implementation of EV technologies. Full article

(This article belongs to the Special Issue Featured Review Papers in Electrical and Autonomous Vehicles)

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11 pages, 2565 KiB

Open AccessArticle

Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning

by Ye Jin Kim, Jung Ho Song, Ki Hwan Cho, Jong Hyeon Shin, Jong Sik Kim, Jung Sik Yoon and Sang Jeen Hong

Electronics 2024, 13(17), 3577; https://doi.org/10.3390/electronics13173577 - 9 Sep 2024

Viewed by 790

Abstract

Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data [...] Read more.

Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emission spectroscopy (OES) data during the SiO₂ etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model. Full article

(This article belongs to the Special Issue Machine Learning in Electronic and Biomedical Engineering, 3rd Edition)

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14 pages, 3238 KiB

Open AccessArticle

An Evaluation of the Autonomic Nervous Activity and Psychomotor Vigilance Level for Smells in the Work Booth

by Emi Yuda, Aoi Otani, Atsushi Yamada and Yutaka Yoshida

Electronics 2024, 13(17), 3576; https://doi.org/10.3390/electronics13173576 - 9 Sep 2024

Viewed by 795

Abstract

In this study, we investigated the effects of the smell environment in the work booth on autonomic nervous activity (ANS) and psychomotor vigilance levels (PVLs) using linalool (LNL) and trans-2-nonenal (T2N). The subjects were six healthy males (31 ± 6 years old) and [...] Read more.

In this study, we investigated the effects of the smell environment in the work booth on autonomic nervous activity (ANS) and psychomotor vigilance levels (PVLs) using linalool (LNL) and trans-2-nonenal (T2N). The subjects were six healthy males (31 ± 6 years old) and six healthy females (24 ± 5 years old). They sat in the work booth filled with the smells of LNL and T2N for 10 min, and their electrocardiograms (ECGs), skin conductance levels, pulse wave variabilities, skin temperatures, and seat pressure distributions were measured. In addition, the orthostatic load test (OLT) and psychomotor vigilance test (PVT) were performed before and after entering the work booth, and a subjective evaluation of the smell was also performed after the experiment. This paper focused on ECG and PVT data and analyzed changes in heart rate variability indices and PVT scores. Males felt slightly comfortable with the LNL smell and showed promoted sympathetic nerve activity in the OLT after the smell presentation. Females felt slightly uncomfortable with the T2N smell and showed promoted sympathetic nerve activity and a decrease in PVT scores in the OLT after the smell presentation. Gender differences were observed in ANS and PVLs, and it is possible that the comfort of LNL increased sympathetic nervous activity in males, while the uncomfortableness of T2N may have reduced work performance in females. Full article

(This article belongs to the Special Issue Internet of Things, Embedded Solutions, and Edge Intelligence for Smart Health)

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45 pages, 31956 KiB

Open AccessArticle

Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools

by Zede Zhu, Yiran Sun and Barmak Honarvar Shakibaei Asli

Electronics 2024, 13(17), 3575; https://doi.org/10.3390/electronics13173575 - 9 Sep 2024

Viewed by 2906

Abstract

The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and [...] Read more.

The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics. Full article

(This article belongs to the Special Issue Image Segmentation, 2nd Edition)

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24 pages, 4734 KiB

Open AccessFeature PaperArticle

A Benchmark Evaluation of Multilingual Large Language Models for Arabic Cross-Lingual Named-Entity Recognition

by Mashael Al-Duwais, Hend Al-Khalifa and Abdulmalik Al-Salman

Electronics 2024, 13(17), 3574; https://doi.org/10.3390/electronics13173574 - 9 Sep 2024

Viewed by 1091

Abstract

Multilingual large language models (MLLMs) have demonstrated remarkable performance across a wide range of cross-lingual Natural Language Processing (NLP) tasks. The emergence of MLLMs made it possible to achieve knowledge transfer from high-resource to low-resource languages. Several MLLMs have been released for cross-lingual [...] Read more.

Multilingual large language models (MLLMs) have demonstrated remarkable performance across a wide range of cross-lingual Natural Language Processing (NLP) tasks. The emergence of MLLMs made it possible to achieve knowledge transfer from high-resource to low-resource languages. Several MLLMs have been released for cross-lingual transfer tasks. However, no systematic evaluation comparing all models for Arabic cross-lingual Named-Entity Recognition (NER) is available. This paper presents a benchmark evaluation to empirically investigate the performance of the state-of-the-art multilingual large language models for Arabic cross-lingual NER. Furthermore, we investigated the performance of different MLLMs adaptation methods to better model the Arabic language. An error analysis of the different adaptation methods is presented. Our experimental results indicate that GigaBERT outperforms other models for Arabic cross-lingual NER, while language-adaptive pre-training (LAPT) proves to be the most effective adaptation method across all datasets. Our findings highlight the importance of incorporating language-specific knowledge to enhance the performance in distant language pairs like English and Arabic. Full article

(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and It’s Applications)

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13 pages, 12402 KiB

Open AccessFeature PaperArticle

Enhanced Coil Design for Inductive Power-Transfer-Based Power Supply in Medium-Voltage Direct Current Sensors

by Seungjin Jo, Dong-Hee Kim and Jung-Hoon Ahn

Electronics 2024, 13(17), 3573; https://doi.org/10.3390/electronics13173573 - 9 Sep 2024

Viewed by 669

Abstract

This paper presents an integrated coil design method for inductive power-transfer (IPT) systems. Because a medium-voltage direct current (MVDC) distribution network transmits power at relatively high voltages (typically in the tens of kV), accurate fault diagnosis using high-performance sensors is crucial to improve [...] Read more.

This paper presents an integrated coil design method for inductive power-transfer (IPT) systems. Because a medium-voltage direct current (MVDC) distribution network transmits power at relatively high voltages (typically in the tens of kV), accurate fault diagnosis using high-performance sensors is crucial to improve the safety of MVDC distribution networks. With the increasing power consumption of high-performance sensors, conventional power supplies using optical converters with 5 W-class output characteristics face limitations in achieving the rated output power. Therefore, this paper proposes a safe and reliable power supply method using the principle of IPT to securely maintain the insulation distance between the distribution network and the current sensor-supply line. A 100 W prototype IPT system is investigated, and its feasibility is validated by comparing its performance with conventional optical converters. Full article

(This article belongs to the Special Issue New Horizons and Recent Advances of Power Electronics)

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23 pages, 7018 KiB

Open AccessReview

2D and Quasi-2D Halide Perovskite-Based Resistive Switching Memory Systems

by Hyojung Kim, Daijoon Hyun, Muhammad Hilal, Zhicheng Cai and Cheon Woo Moon

Electronics 2024, 13(17), 3572; https://doi.org/10.3390/electronics13173572 - 8 Sep 2024

Viewed by 937

Abstract

Resistive switching (RS) memory devices are gaining recognition as data storage devices due to the significant interest in their switching material, Halide perovskite (HP). The electrical characteristics include hysteresis in its current–voltage (I–V) relationship. It can be attributed to [...] Read more.

Resistive switching (RS) memory devices are gaining recognition as data storage devices due to the significant interest in their switching material, Halide perovskite (HP). The electrical characteristics include hysteresis in its current–voltage (I–V) relationship. It can be attributed to the production and migration of defects. This property allows HPs to be used as RS materials in memory devices. However, 3D HPs are vulnerable to moisture and the surrounding environment, making their devices more susceptible to deterioration. The potential of two-dimensional (2D)/quasi-2D HPs for optoelectronic applications has been recognized, making them a viable alternative to address current restrictions. Two-dimensional/quasi-2D HPs are created by including extended organic cations into the ABX₃ frameworks. By adjusting the number of HP layers, it is possible to control the optoelectronic properties to achieve specific features for certain applications. This article presents an overview of 2D/quasi-2D HPs, including their structures, binding energies, and charge transport, compared to 3D HPs. Next, we discuss the operational principles, RS modes (bipolar and unipolar switching), in RS memory devices. Finally, there have been notable and recent breakthroughs in developing RS memory systems using 2D/quasi-2D HPs. Full article

(This article belongs to the Special Issue Advanced Materials for Intelligent Electronics)

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16 pages, 8241 KiB

Open AccessArticle

Research on Space Operation Control of Air Float Satellite Simulator Based on Constraints Aware Particle Filtering-Nonlinear Model Predictive Control

by Lingfeng Xu, Danhe Chen, Chuangge Wang and Wenhe Liao

Electronics 2024, 13(17), 3571; https://doi.org/10.3390/electronics13173571 - 8 Sep 2024

Viewed by 726

Abstract

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed [...] Read more.

This paper addresses the challenges of close proximity operations, such as rendezvous, docking, and fly-around maneuvers for micro/nano satellites, which require high control precision under the low power and limited computational capabilities of spacecraft. Firstly, a three-degree-of-freedom air float simulator platform is designed for ground-based experiments. Subsequently, model predictive controllers based on constraints aware of particle filtering (CAPF-NMPC) are developed for executing operations such as approach, fly-around, and docking maneuvers. The results validate the effectiveness of the experimental system, demonstrating position control accuracy less than 0.03 m and attitude control accuracy less than 3°, maintaining lower computational resource consumption. This study offers a practical solution for the onboard deployment of optimized control algorithms, highlighting significant value for further engineering applications. Full article

(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))

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13 pages, 463 KiB

Open AccessFeature PaperArticle

Deep Learning-Based Joint Beamforming Design for Multi-Hop Reconfigurable Intelligent Surface (RIS)-Aided Communication Systems

by Xiao Chen, Jiaoyang Ye, Yuxuan Wei, Jianfeng Shi and Jianyue Zhu

Electronics 2024, 13(17), 3570; https://doi.org/10.3390/electronics13173570 - 8 Sep 2024

Viewed by 789

Abstract

Reconfigurable intelligent surface (RIS) is one of the promising technologies for sixth generation communications due to its advantages including energy saving, high spectral efficiency, etc. However, the non-convex joint beamforming design is a challenge, especially in the multi-hop RIS-assisted communication system. This paper [...] Read more.

Reconfigurable intelligent surface (RIS) is one of the promising technologies for sixth generation communications due to its advantages including energy saving, high spectral efficiency, etc. However, the non-convex joint beamforming design is a challenge, especially in the multi-hop RIS-assisted communication system. This paper proposes a deep learning-based joint beamforming (DLBF) design, aiming to maximize the system data rate for multi-hop RIS-aided communication systems. The proposed DLBF design consists of the reflection matrices design of all RISs and the transmit beamforming design at the base station, which has a reduced computational complexity. Numerical results show that the proposed DLBF can achieve

1.8

bit/s/Hz sum rate gain compared to the conventional beamforming method for the two-user scenario, which can be enhanced by large-scale users. The sum rate performance can be improved by increasing the number of RISs due to the reflection gain, and corresponding results provide a guidance of the multi-hop number selection for further investigation. Full article

(This article belongs to the Special Issue Millimeter-Wave and Terahertz Technologies for Wireless Communications)

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21 pages, 5840 KiB

Open AccessArticle

Enhancing Linux System Security: A Kernel-Based Approach to Fileless Malware Detection and Mitigation

by Min-Hao Wu, Fu-Hau Hsu, Jian-Hung Huang, Keyuan Wang, Yan-Ling Hwang, Hao-Jyun Wang, Jian-Xin Chen, Teng-Chuan Hsiao and Hao-Tsung Yang

Electronics 2024, 13(17), 3569; https://doi.org/10.3390/electronics13173569 - 8 Sep 2024

Viewed by 1253

Abstract

In the late 20th century, computer viruses emerged as powerful malware that resides permanently in target hosts. For a virus to function, it must load into memory from persistent storage, such as a file on a hard drive. Due to the significant destructive [...] Read more.

In the late 20th century, computer viruses emerged as powerful malware that resides permanently in target hosts. For a virus to function, it must load into memory from persistent storage, such as a file on a hard drive. Due to the significant destructive potential of viruses, numerous defense measures have been developed to protect computer systems. Among these, antivirus software is one of the most recognized and widely used. Typically, antivirus solutions rely on static analysis (signature-based) technologies to detect infections in files stored on permanent storage devices, such as hard drives or USB (Universal Serial Bus) flash drives. However, a new breed of malware, fileless malware, has been designed to evade detection and enhance durability. Fileless malware resides solely in the memory of the target hosts, circumventing traditional antivirus software, which cannot access or analyze processes executed directly from memory. This study proposes the Check-on-Execution (CoE) kernel-based approach to detect fileless malware on Linux systems. CoE intervenes by suspending code execution before a program executes code from a process’s writable and executable memory area. To prevent the execution of fileless malware, CoE extracts the code from memory, packages it with an ELF (Executable and Linkable Format) header to create an ELF file, and uses VirusTotal for analysis. Experimental results demonstrate that CoE significantly enhances a Linux system’s ability to defend against fileless malware. Additionally, CoE effectively protects against shell code injection attacks, including buffer and memory overflows, and can handle packed malware. However, it is important to note that this study focuses exclusively on fileless malware, and further research is needed to address other types of malware. Full article

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36 pages, 2275 KiB

Open AccessReview

Blockchain Forensics: A Systematic Literature Review of Techniques, Applications, Challenges, and Future Directions

by Hany F. Atlam, Ndifon Ekuri, Muhammad Ajmal Azad and Harjinder Singh Lallie

Electronics 2024, 13(17), 3568; https://doi.org/10.3390/electronics13173568 - 8 Sep 2024

Viewed by 2203

Abstract

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link [...] Read more.

Blockchain technology has gained significant attention in recent years for its potential to revolutionize various sectors, including finance, supply chain management, and digital forensics. While blockchain’s decentralization enhances security, it complicates the identification and tracking of illegal activities, making it challenging to link blockchain addresses to real-world identities. Also, although immutability protects against tampering, it introduces challenges for forensic investigations as it prevents the modification or deletion of evidence, even if it is fraudulent. Hence, this paper provides a systematic literature review and examination of state-of-the-art studies in blockchain forensics to offer a comprehensive understanding of the topic. This paper provides a comprehensive investigation of the fundamental principles of blockchain forensics, exploring various techniques and applications for conducting digital forensic investigations in blockchain. Based on the selected search strategy, 46 articles (out of 672) were chosen for closer examination. The contributions of these articles were discussed and summarized, highlighting their strengths and limitations. This paper examines the selected papers to identify diverse digital forensic frameworks and methodologies used in blockchain forensics, as well as how blockchain-based forensic solutions have enhanced forensic investigations. In addition, this paper discusses the common applications of blockchain-based forensic frameworks and examines the associated legal and regulatory challenges encountered in conducting a forensic investigation within blockchain systems. Open issues and future research directions of blockchain forensics were also discussed. This paper provides significant value for researchers, digital forensic practitioners, and investigators by providing a comprehensive and up-to-date review of existing research and identifying key challenges and opportunities related to blockchain forensics. Full article

(This article belongs to the Special Issue Blockchain and loT: Security and Privacy Advancements, Integration, Opportunities and Open Issues)

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22 pages, 7586 KiB

Open AccessArticle

Bi-Level Optimal Configuration of Electric Thermal Storage Boilers in Thermal–Electrical Integrated Energy System

by Xiaoming Zhang, Jiaoyang Feng, Guangzhe Liang, Chonglei Ding, Peihong Yang and Xin Zhang

Electronics 2024, 13(17), 3567; https://doi.org/10.3390/electronics13173567 - 8 Sep 2024

Viewed by 627

Abstract

Electric thermal storage boilers (ETSBs) are important devices in enhancing the electric–thermal decoupling ability and spatiotemporal transfer of integrated energy system (IES), which is beneficial for improving system flexibility and energy utilization efficiency. In order to obtain more accurate and comprehensive results, a [...] Read more.

Electric thermal storage boilers (ETSBs) are important devices in enhancing the electric–thermal decoupling ability and spatiotemporal transfer of integrated energy system (IES), which is beneficial for improving system flexibility and energy utilization efficiency. In order to obtain more accurate and comprehensive results, a bi-level optimal model is proposed to study the site selection and capacity configuration of ETSB in IES based on the established mathematical model of ETSB. The objective of upper-level optimization of the model is obtaining the lowest energy supply cost when configuring the location and capacity of ETSB, while the lower-level model optimizes the operation scheduling with the goal of obtaining the lowest operational cost. The mixed-integer linear programming method and the genetic algorithm method are selected to obtain the optimal model. To illustrate the effectiveness and advantages of the proposed method, case studies are carried out. The optimal configuration scheme for an ETSB is obtained by comparing the lowest energy supply cost under different configuration parameters. Furthermore, the impact of an ETSB on the system is also analyzed based on the variations in energy balance, abandoned energy, and energy allocation before and after configuring the ETSB. Full article

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23 pages, 8323 KiB

Open AccessArticle

A New Cascaded Multilevel Inverter for Modular Structure and Reduced Passive Components

by Durbanjali Das, Bidyut Mahato, Bikramaditya Chandan, Hitesh Joshi, Kailash Kumar Mahto, Priyanath Das, Georgios Fotis, Vasiliki Vita and Michael Mann

Electronics 2024, 13(17), 3566; https://doi.org/10.3390/electronics13173566 - 8 Sep 2024

Viewed by 805

Abstract

In high-power applications, achieving adequate power quality in power converter design is accomplished by utilizing multilevel inverters instead of using two-level and three-level inverters. The device generates a sinusoidal output voltage, which results in reduced total harmonic distortion and lower voltage stress on [...] Read more.

In high-power applications, achieving adequate power quality in power converter design is accomplished by utilizing multilevel inverters instead of using two-level and three-level inverters. The device generates a sinusoidal output voltage, which results in reduced total harmonic distortion and lower voltage stress on the switches and leads to lower electromagnetic interference, making it suitable for use in renewable energy applications. However, to illustrate the advantages mentioned above, a significant number of switching devices and DC sources are necessary while raising the voltage levels. This article proposes an asymmetrical voltage generation method, which operates in a ratio of 1:5 and generates 25 levels using 11 power switches. The topology is modular in structure, and each module has a lower component count, which significantly reduces the overall cost. The proposed topology is capable of generating negative output voltage levels without the use of an H-bridge configuration, where only three switches are used to generate any voltage levels. The functionality of the developed module is amended by fixing different voltage values in DC sources. This article also presents a comprehensive examination of the circuit and the functioning of various voltage levels. The advantages of the proposed inverter have been demonstrated by comparative research with the currently existing MLI topologies. Ultimately, both the simulation and experimental findings validated the practical capabilities. Full article

(This article belongs to the Section Power Electronics)

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23 pages, 8741 KiB

Open AccessArticle

Current-Mode Control of a Distributed Buck Converter with a Lossy Transmission Line

by Klaus Röbenack and Daniel Gerbet

Electronics 2024, 13(17), 3565; https://doi.org/10.3390/electronics13173565 - 8 Sep 2024

Viewed by 589

Abstract

This article presents a buck converter in which the inductor has been replaced by a transmission line. This approach would be practically conceivable if the power supply and load had a greater spatial distance. Alternatively, the model derived in this way could also [...] Read more.

This article presents a buck converter in which the inductor has been replaced by a transmission line. This approach would be practically conceivable if the power supply and load had a greater spatial distance. Alternatively, the model derived in this way could also be regarded as an intermediate model in order to replace a power coil via discretization with a larger number of smaller coils and capacitors. In the time domain, this new converter can be described by a system of coupled partial and ordinary differential equations. In the frequency domain, a transcendental transfer function is obtained. For comparison with an equivalently parameterized conventional converter, Padé approximants are derived. A linear controller is designed for the converter topology under consideration. Full article

(This article belongs to the Special Issue Control and Optimization of Power Converters and Drives)

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24 pages, 7323 KiB

Open AccessArticle

AID-YOLO: An Efficient and Lightweight Network Method for Small Target Detector in Aerial Images

by Yuwen Li, Jiashuo Zheng, Shaokun Li, Chunxi Wang, Zimu Zhang and Xiujian Zhang

Electronics 2024, 13(17), 3564; https://doi.org/10.3390/electronics13173564 - 8 Sep 2024

Viewed by 1024

Abstract

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the [...] Read more.

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the [email protected] metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the [email protected] metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection. Full article

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24 pages, 5079 KiB

Open AccessArticle

Leveraging Generative AI in Short Document Indexing

by Sara Bouzid and Loïs Piron

Electronics 2024, 13(17), 3563; https://doi.org/10.3390/electronics13173563 - 8 Sep 2024

Viewed by 1215

Abstract

The efficiency of information retrieval systems primarily depends on the effective representation of documents during query processing. This representation is mainly constructed from relevant document terms identified and selected during their indexing, which are then used for retrieval. However, when documents contain only [...] Read more.

The efficiency of information retrieval systems primarily depends on the effective representation of documents during query processing. This representation is mainly constructed from relevant document terms identified and selected during their indexing, which are then used for retrieval. However, when documents contain only a few features, such as in short documents, the resulting representation may be information-poor due to a lack of index terms and their lack of relevance. Although document representation can be enriched using techniques like word embeddings, these techniques require large pre-trained datasets, which are often unavailable in the context of domain-specific short documents. This study investigates a new approach to enrich document representation during indexing using generative AI. In the proposed approach, relevant terms extracted from documents and preprocessed for indexing are enriched with a list of key terms suggested by a large language model (LLM). After conducting a small benchmark of several renowned LLM models for key term suggestions from a set of short texts, the GPT-4o model was chosen to experiment with the proposed indexing approach. The findings of this study yielded notable results, demonstrating that generative AI can efficiently fill the knowledge gap in document representation, regardless of the retrieval technique used. Full article

(This article belongs to the Section Computer Science & Engineering)

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19 pages, 1547 KiB

Open AccessReview

Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices

by Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara and Qinghe Zheng

Electronics 2024, 13(17), 3562; https://doi.org/10.3390/electronics13173562 - 8 Sep 2024

Viewed by 2472

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With [...] Read more.

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems. Full article

(This article belongs to the Special Issue Applied Machine Learning in Intelligent Systems)

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14 pages, 1615 KiB

Open AccessArticle

Multi-Dimensional Resource Allocation for Covert Communications in Multi-Beam Low-Earth-Orbit Satellite Systems

by Renge Wang, Minghao Chen, Luyan Xu, Zhong Wen, Yiyang Wei and Shice Li

Electronics 2024, 13(17), 3561; https://doi.org/10.3390/electronics13173561 - 8 Sep 2024

Viewed by 876

Abstract

Satellite communication systems, especially multi-beam low-Earth-orbit (LEO) satellites, could cater to the needs of different industrial applications through flexible resource allocation. Unfortunately, due to the wide coverage of LEO satellites, the data exchange within the LEO satellite networks suffers from the risk of [...] Read more.

Satellite communication systems, especially multi-beam low-Earth-orbit (LEO) satellites, could cater to the needs of different industrial applications through flexible resource allocation. Unfortunately, due to the wide coverage of LEO satellites, the data exchange within the LEO satellite networks suffers from the risk of eavesdropping and malicious jamming, which could severely degrade the performance of the industrial production process. To address such challenges, this paper introduces a multi-dimensional resource allocation strategy to facilitate covert communication within the multi-beam LEO satellite network. Our approach ensures the rate requirements of different user equipments while preventing the detection of communication signals by an eavesdropping geostationary orbit (GEO) satellite. Specifically, we formulate an optimization problem that jointly optimizes satellite beam-hopping scheduling, frequency band allocation, and the transmit power of different user equipments, under the covertness constraint. By introducing auxiliary binary variables, we transform this optimization problem into a Mixed-Integer Linear Programming (MILP) problem, which allows us to utilize machine learning-based techniques for efficient solution finding. The simulation results demonstrate the effectiveness of our proposed scheme. Full article

(This article belongs to the Special Issue Intelligent Resource Allocation Methods for Next Generation Wireless Cellular Systems)

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23 pages, 4034 KiB

Open AccessArticle

A Unified Model of a Virtual Synchronous Generator for Transient Stability Analysis

by Ming Li, Chengzhi Wei, Ruifeng Zhao, Jiangang Lu, Yizhe Chen and Wanli Yang

Electronics 2024, 13(17), 3560; https://doi.org/10.3390/electronics13173560 - 7 Sep 2024

Cited by 1 | Viewed by 632

Abstract

A virtual synchronous generator (VSG) is prone to transient instability under a grid fault, which leads to the loss of synchronization between the new energy converter and grid, and threatens the operation safety of high-proportion new energy grids. There are a variety of [...] Read more.

A virtual synchronous generator (VSG) is prone to transient instability under a grid fault, which leads to the loss of synchronization between the new energy converter and grid, and threatens the operation safety of high-proportion new energy grids. There are a variety of control models in the existing VSG control, including active and reactive power models, which lead to their different transient stabilities. However, the evolution characteristics, correlation between different models of VSG, and the internal mechanism affecting transient stability have not been fully studied. To this effect, this paper analyzes their evolution characteristics based on the existing mainstream VSG control models and establishes a unified VSG model and its equivalent correspondence with other models. Then, the phase plane method is used to comprehensively analyze and compare the transient stability of the VSG unified model with other models. It is pointed out that the key factors affecting the transient stability of different models are three links of primary frequency regulation, reactive power regulation and reactive power tracking. Finally, the correctness of the established VSG unified model and the conclusion of transient stability analysis is verified by experiments. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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37 pages, 4978 KiB

Open AccessFeature PaperArticle

Harmonic State Estimation in Power Systems Using the Jaya Algorithm

by Walace do Nascimento Sepulchro and Lucas Frizera Encarnação

Electronics 2024, 13(17), 3559; https://doi.org/10.3390/electronics13173559 - 7 Sep 2024

Viewed by 624

Abstract

The increasing use of nonlinear loads in power systems introduces voltage and current components at non-fundamental frequencies, leading to harmonic distortion, which negatively impacts electrical and electronic devices. A common mitigation strategy involves identifying harmonic sources and installing filters nearby. However, due to [...] Read more.

The increasing use of nonlinear loads in power systems introduces voltage and current components at non-fundamental frequencies, leading to harmonic distortion, which negatively impacts electrical and electronic devices. A common mitigation strategy involves identifying harmonic sources and installing filters nearby. However, due to the high cost of power quality (PQ) meters, comprehensive harmonic level monitoring across the entire power system is impractical. To address this, various methodologies for Harmonic State Estimation (HSE) have been developed, which estimate distortion levels on unmonitored system buses using data from a minimal set of monitored ones. Many HSE techniques rely on optimization algorithms with numerous tuning parameters, complicating their application. This paper proposes a novel methodology for fundamental frequency power flow and harmonic state estimation using the Jaya algorithm, which is characterized by fewer tuning parameters for easier adjustment. It also introduces a strategy to determine the minimal number of buses that need monitoring to achieve system observability. The methodology is validated on the IEEE-14 and IEEE-30 bus systems, demonstrating its effectiveness. The results of the proposed methodology are compared with those obtained using Evolutionary Strategies (ESs), highlighting its enhanced accuracy and computational efficiency. Full article

(This article belongs to the Special Issue Compatibility, Power Electronics and Power Engineering)

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19 pages, 1665 KiB

Open AccessArticle

Efficient Headline Generation with Hybrid Attention for Long Texts

by Wenjin Wan, Cong Zhang and Lan Huang

Electronics 2024, 13(17), 3558; https://doi.org/10.3390/electronics13173558 - 7 Sep 2024

Viewed by 590

Abstract

Headline generation aims to condense key information from an article or a document into a concise one-sentence summary. The Transformer structure is in general effective for such tasks, yet it suffers from a dramatic increase in training time and GPU consumption as the [...] Read more.

Headline generation aims to condense key information from an article or a document into a concise one-sentence summary. The Transformer structure is in general effective for such tasks, yet it suffers from a dramatic increase in training time and GPU consumption as the input text length grows. To address this problem, a hybrid attention mechanism is proposed. Both local and global semantic information among words are modeled in a way that significantly improves training efficiency, especially for long text. Effectiveness is not sacrificed; in fact, fluency and semantic coherence of the generated headlines are enhanced. Experimental results on an open benchmark dataset show that, compared to the baseline model’s best performance, the proposed model obtains a 14.7%, 16.7%, 14.4% and 9.1% increase in the F1 values of the ROUGE-1, the ROUGE-2, the ROUGE-L and the ROUGE-WE metrics, respectively. The semantic coherence of the generated text is also improved, as shown by a 2.8% improvement in the BERTScore’s F1 value. These results show that the effectiveness of the proposed headline generation model with the hybrid attention mechanism is also improved. The hybrid attention mechanism could provide references for relevant text generation tasks. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 9439 KiB

Open AccessArticle

MFAD-RTDETR: A Multi-Frequency Aggregate Diffusion Feature Flow Composite Model for Printed Circuit Board Defect Detection

by Zhihua Xie and Xiaowei Zou

Electronics 2024, 13(17), 3557; https://doi.org/10.3390/electronics13173557 - 7 Sep 2024

Viewed by 1176

Abstract

To address the challenges of excessive model parameters and low detection accuracy in printed circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved RTDETR (Real-Time Detection, Embedding and Tracking) method, named MFAD-RTDETR. Specifically, the [...] Read more.

To address the challenges of excessive model parameters and low detection accuracy in printed circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved RTDETR (Real-Time Detection, Embedding and Tracking) method, named MFAD-RTDETR. Specifically, the proposed model introduces the designed Detail Feature Retainer (DFR) into the original RTDETR backbone to capture and retain local details. Subsequently, based on the Mamba architecture, the Visual State Space (VSS) module is integrated to enhance global attention while reducing the original quadratic complexity to a linear level. Furthermore, by exploiting the deformable attention mechanism, which dynamically adjusts reference points, the model achieves precise localization of target defects and improves the accuracy of the transformer in complex visual tasks. Meanwhile, a receptive field synthesis mechanism is incorporated to enrich multi-scale semantic information and reduce parameter complexity. In addition, the scheme proposes a novel Multi-frequency Aggregation and Diffusion feature composite paradigm (MFAD-feature composite paradigm), which consists of the Aggregation Diffusion Fusion (ADF) module and the Refiner Feature Composition (RFC) module. It aims to strengthen features with fine-grained awareness while preserving a certain level of global attention. Finally, the Wise IoU (WIoU) dynamic nonmonotonic focusing mechanism is used to reduce competition among high-quality anchor boxes and mitigate the effects of the harmful gradients from low-quality examples, thereby concentrating on anchor boxes of average quality to promote the overall performance of the detector. Extensive experiments are conducted on the PCB defect dataset released by Peking University to validate the effectiveness of the proposed model. The experimental results show that our approach achieves the 97.0% and 51.0% performance in mean Average Precision (mAP)@0.5 and [email protected]:0.95, respectively, which significantly outperforms the original RTDETR. Moreover, the model reduces the number of parameters by approximately 18.2% compared to the original RTDETR. Full article

(This article belongs to the Special Issue Deep Learning for Computer Vision Application)

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16 pages, 5512 KiB

Open AccessArticle

Half-Wave Phase Shift Modulation for Hybrid Modular Multilevel Converter with Wide-Range Operation

by Junchao Ma, Yan Peng, Zimeng Su, Yilei Gu, Qiulong Ni, Ying Yang, Yi Wang and Jianing Liu

Electronics 2024, 13(17), 3556; https://doi.org/10.3390/electronics13173556 - 7 Sep 2024

Viewed by 651

Abstract

Hybrid modular multilevel converters (MMCs), which combine submodule chain links and device series switches, offer advantages such as lower costs and smaller volumes compared with MMCs. However, the hybrid MMCs only operate at a fixed modulation ratio, potentially compromising system adjustment ability. This [...] Read more.

Hybrid modular multilevel converters (MMCs), which combine submodule chain links and device series switches, offer advantages such as lower costs and smaller volumes compared with MMCs. However, the hybrid MMCs only operate at a fixed modulation ratio, potentially compromising system adjustment ability. This paper presents a half-wave phase shift modulation (HPSM) strategy aimed at extending the operation range of a hybrid MMC. First, the commutation angle is introduced as a control variable to change the fixed voltage modulation ratio. The energy balance of the converter is completed by adjusting the commutation angle. Then, the operation performance for the half-wave alternating multilevel converter (HAMC) with the proposed HPSM strategy is analyzed. Finally, the full-scale simulations are carried out to verify the theoretical analysis and the feasibility of the proposed control strategy. Compared to the third-order harmonic current injection (THCI) strategy, HPSM reduces operating losses by 50% and demonstrates superior control performance. Full article

(This article belongs to the Topic Application of Innovative Power Electronic Technologies, 2nd Edition)

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22 pages, 4528 KiB

Open AccessArticle

Dynamic Attitude Inertial Measurement Method for Typical Regions of Deck Deformation

by Bo Zhao, Xiuwei Xia, Tianyu Wang and Wei Gao

Electronics 2024, 13(17), 3555; https://doi.org/10.3390/electronics13173555 - 6 Sep 2024

Viewed by 635

Abstract

Due to the deformation of ships, it becomes difficult to ensure the accuracy of attitude measurement in typical areas on the deck, which seriously impacts the safety and operational efficiency of shipborne equipment. To address this issue, this paper presents a parameter identification [...] Read more.

Due to the deformation of ships, it becomes difficult to ensure the accuracy of attitude measurement in typical areas on the deck, which seriously impacts the safety and operational efficiency of shipborne equipment. To address this issue, this paper presents a parameter identification method for dynamic deformation models based on angle increment differences and introduces the related vector machine (RVM) algorithm for online estimation of dynamic deformation model parameters. In view of the truncation error and non-Gaussian noise of the model, this article proposes a dynamic attitude measurement method based on model predictive filtering (MPF), constructs a dynamic measurement model using Rodrigues parameters in an inertial frame, and designs a maximum correlation entropy (MCE) robust filter to achieve robust estimation of deck dynamic deformation. The performance of the method is verified through simulation analysis and shipborne experiments. The comparative results indicate that, compared with existing methods, the proposed improved deck dynamic attitude measurement algorithm based on model prediction (IDAM) can substantially enhance the accuracy of attitude measurement in the presence of deck dynamic deformations. Full article

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24 pages, 4205 KiB

Open AccessArticle

Using Mixed Reality for Control and Monitoring of Robot Model Based on Robot Operating System 2

by Dominik Janecký, Erik Kučera, Oto Haffner, Erika Výchlopeňová and Danica Rosinová

Electronics 2024, 13(17), 3554; https://doi.org/10.3390/electronics13173554 - 6 Sep 2024

Viewed by 681

Abstract

This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the [...] Read more.

This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the Unity game engine alongside the Mixed Reality Toolkit (MRTK) to create an immersive mixed reality application. The project uses the Turtlebot 3 Burger model robot, simulated within the Gazebo virtual environment, as a representative mechatronic system for demonstration purposes. Communication between the mixed reality application and ROS 2 is facilitated through a publish–subscribe mechanism, utilizing ROS TCP Connector for message serialization between nodes. This interface not only enhances the user experience by allowing for the real-time monitoring and control of the robotic system but also aligns with the principles of Industry 5.0, emphasizing human-centric and inclusive technological advancements. The practical outcomes of this research include a fully functional mixed reality application that integrates seamlessly with ROS 2, showcasing the potential of mixed reality technologies in advancing the field of industrial automation and human–machine interaction. Full article

(This article belongs to the Special Issue Advanced Industry 4.0/5.0: Intelligence and Automation)

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15 pages, 1777 KiB

Open AccessArticle

Going beyond API Calls in Dynamic Malware Analysis: A Novel Dataset

by Slaviša Ilić, Milan Gnjatović, Ivan Tot, Boriša Jovanović, Nemanja Maček and Marijana Gavrilović Božović

Electronics 2024, 13(17), 3553; https://doi.org/10.3390/electronics13173553 - 6 Sep 2024

Viewed by 857

Abstract

Automated sandbox-based analysis systems are dominantly focused on sequences of API calls, which are widely acknowledged as discriminative and easily extracted features. In this paper, we argue that an extension of the feature set beyond API calls may improve the malware detection performance. [...] Read more.

Automated sandbox-based analysis systems are dominantly focused on sequences of API calls, which are widely acknowledged as discriminative and easily extracted features. In this paper, we argue that an extension of the feature set beyond API calls may improve the malware detection performance. For this purpose, we apply the Cuckoo open-source sandbox system, carefully configured for the production of a novel dataset for dynamic malware analysis containing 22,200 annotated samples (11,735 benign and 10,465 malware). Each sample represents a full-featured report generated by the Cuckoo sandbox when a corresponding binary file is submitted for analysis. To support our position that the discriminative power of the full-featured sandbox reports is greater than the discriminative power of just API call sequences, we consider samples obtained from binary files whose execution induced API calls. In addition, we derive an additional dataset from samples in the full-featured dataset, whose samples contain only information on API calls. In a three-way factorial design experiment (considering the feature set, the feature representation technique, and the random forest model hyperparameter settings), we trained and tested a set of random forest models in a two-class classification task. The obtained results demonstrate that resorting to full-featured sandbox reports improves malware detection performance. The accuracy of 95.56 percent obtained for API call sequences was increased to 99.74 percent when full-featured sandbox reports were considered. Full article

(This article belongs to the Special Issue Intelligent Solutions for Network and Cyber Security)

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