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Volume 12
Issue 16
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Electronics, Volume 12, Issue 16 (August-2 2023) – 161 articles

Cover Story (view full-size image): This work describes a cost-effective compact solution for ultra-low-frequency impedance measurements, operating from 1 mHz to 250 kHz. A fully analog circuit has been designed and tested to be coupled to a lock-in amplifier. The system is based on a Howland converter cascaded by a precision current divider in order to set the conversion factor at 0.1, 1, or 100 μA/V. A feedback network is inserted to null the voltage drift induced by leakage currents and offset voltages, which allows the measurement of low-capacitance loads even in the presence of high-voltage biasing. The circuit is likely to be used for the conditioning of both resistive and capacitive sensors, and it represents an effective solution for the implementation of a portable instrument for biosensor characterizations. View this paper
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15 pages, 6359 KiB  
Article
Measuring the Impact of ChatGPT on Fostering Concept Generation in Innovative Product Design
by Stefano Filippi
Electronics 2023, 12(16), 3535; https://doi.org/10.3390/electronics12163535 - 21 Aug 2023
Cited by 8 | Viewed by 2641
Abstract
The growing demand for innovative and user-centric product design has led to a growing need for effective idea generation methods. In recent years, natural language processing (NLP) tools such as ChatGPT have emerged as a promising solution for supporting idea generation in various [...] Read more.
The growing demand for innovative and user-centric product design has led to a growing need for effective idea generation methods. In recent years, natural language processing (NLP) tools such as ChatGPT have emerged as a promising solution for supporting idea generation in various domains. This paper investigates a framework for studying the role of ChatGPT in facilitating the ideation process in product design. This investigation measures the impact of ChatGPT on the generation of innovative concepts compared to the use of “classic” design methods. An overview of the state-of-the-art idea generation methods in product design opens the paper. Then, the paper highlights some hypotheses about the impact of ChatGPT on innovative product design, aiming for product augmentation by adding features. The paper then describes the design experience in which ChatGPT is used as a tool for concept generation. Finally, the paper analyzes the dataset, using precise metrics to characterize the participants’ performance and compare them. This analysis allows the paper to argue about the validation/rejection of the hypotheses. The paper concludes with a discussion of the implications of the findings and some suggestions for future research. Along with the paper, the Microsoft Excel workbook used to perform the data analysis is available to the readers to perform their own data collection and analysis. The workbook UX has been carefully studied and developed to make it usable by anyone. At the same time, it should be flexible enough to manage several situations characterized by different numbers of participants, product functions to implement, and generated concepts. Full article
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43 pages, 6315 KiB  
Review
A Review of Capacitive Power Transfer Technology for Electric Vehicle Applications
by Jiantao Zhang, Shunyu Yao, Liangyi Pan, Ying Liu and Chunbo Zhu
Electronics 2023, 12(16), 3534; https://doi.org/10.3390/electronics12163534 - 21 Aug 2023
Cited by 2 | Viewed by 1409
Abstract
Electric Vehicle (EV) wireless power transfer technology is an excellent solution to propel EVs forward. The existing wireless power transfer technology for EVs based on Inductive Power Transfer (IPT) technology has the drawbacks of large size, high weight, and high eddy current loss, [...] Read more.
Electric Vehicle (EV) wireless power transfer technology is an excellent solution to propel EVs forward. The existing wireless power transfer technology for EVs based on Inductive Power Transfer (IPT) technology has the drawbacks of large size, high weight, and high eddy current loss, limiting the further application of this technology. Capacitive Power Transfer (CPT) technology, with its advantages of low cost and light weight, has attracted widespread focus in recent years and has great potential in the field of EV wireless power transfer. This paper begins with the principle of CPT, introduces the potential and development history of CPT technology in the field of EV wireless power transfer, and then reviews the coupling mechanism and resonance compensation network of the CPT system to satisfy the requirements of EV wireless power transfer, including the coupling mechanism of EV static power transfer and dynamic power transfer, and the high-performance resonance compensation network to the requirements of EV wireless power transfer. Finally, this paper reviews the existing problems of CPT technology in the field of EV wireless power transfer and summarizes its future development directions. Full article
(This article belongs to the Topic Advanced Wireless Charging Technology)
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23 pages, 12652 KiB  
Article
Enhanced Power Factor Correction and Torque Ripple Mitigation for DC–DC Converter Based BLDC Drive
by Geethu Krishnan, Moshe Sitbon and Shijoh Vellayikot
Electronics 2023, 12(16), 3533; https://doi.org/10.3390/electronics12163533 - 21 Aug 2023
Cited by 3 | Viewed by 1063
Abstract
A novel approach to the design of power factor correction (PFC) and torque ripple minimization in a brushless direct current (BLDC) motor drive with a new pulse width modulation (PWM) technique is demonstrated. The drive was designed to have a better power factor [...] Read more.
A novel approach to the design of power factor correction (PFC) and torque ripple minimization in a brushless direct current (BLDC) motor drive with a new pulse width modulation (PWM) technique is demonstrated. The drive was designed to have a better power factor (PF) and less torque ripple. On the other hand, the modified Zeta converter is used to enhance the power factor of the proposed system. The modified Zeta converter is operated in discontinuous inductor current mode (DICM) by using a voltage follower technique, which only needs a voltage sensor for power factor correction (PFC) operation and DC-link voltage control. The output voltage of the VSI is determined by switching patterns generated by the PWM-ON-PWM switching strategy, and it reduces the torque ripples. The proposed drive is developed and simulated in a MATLAB/Simulink environment. The power factor of 0.9999 is produced by the PFC modified zeta converter topology and the PWM-ON-PWM scheme reduce the torque ripple in the commutation region by 34.2% as compared with the PWM-ON scheme. This demonstrates the effectiveness of the suggested control method. Full article
(This article belongs to the Special Issue New Trends in Power Electronics for Microgrids)
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14 pages, 4256 KiB  
Article
Power Equalization Control Strategy for MMCs in Hybrid-Cascaded UHVDC System
by Lei Liu, Yufei Teng, Xiaopeng Li, Yong Tang and Xiaofeng Jiang
Electronics 2023, 12(16), 3532; https://doi.org/10.3390/electronics12163532 - 21 Aug 2023
Viewed by 632
Abstract
Based on the hybrid-cascaded topology of ultra-high-voltage direct current (UHVDC) engineering, this study clarified the mechanism of unbalanced power generation among modular multilevel converters (MMCs) at the inverter side following the fault of the AC system at the rectifying side, and then proposed [...] Read more.
Based on the hybrid-cascaded topology of ultra-high-voltage direct current (UHVDC) engineering, this study clarified the mechanism of unbalanced power generation among modular multilevel converters (MMCs) at the inverter side following the fault of the AC system at the rectifying side, and then proposed the power equalization strategy for MMCs. By performing closed-loop control on the active power deviation between constant-voltage and constant-power MMCs, it was possible to achieve automatic power equalization among MMCs after the occurrence of a fault so as to avoid the detrimental effect of a single MMC’s power fluctuation on the connected AC system. Meanwhile, the control enabling logic was designed to ensure the reliable input and stable exit of the control strategy throughout the disturbance period. Finally, a PSCAD/EMTDC platform was used to simulate various types of faults in the AC system at the rectifier side in order to validate the effectiveness of the proposed power equalization strategy. Full article
(This article belongs to the Special Issue The Hybrid AC-DC Power System Coordinated Control and Operation Technology)
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32 pages, 16646 KiB  
Review
Augmented Reality: Current and New Trends in Education
by Dorota Kamińska, Grzegorz Zwoliński, Anna Laska-Leśniewicz, Rui Raposo, Mário Vairinhos, Elisabeth Pereira, Frane Urem, Martina Ljubić Hinić, Rain Eric Haamer and Gholamreza Anbarjafari
Electronics 2023, 12(16), 3531; https://doi.org/10.3390/electronics12163531 - 21 Aug 2023
Cited by 4 | Viewed by 5140
Abstract
The educational landscape is an environment prone to change due to the volatile and ever-changing nature of the digital society in which we all live. Although the world moves at different speeds and any generalization is bound to have some exceptions, there is [...] Read more.
The educational landscape is an environment prone to change due to the volatile and ever-changing nature of the digital society in which we all live. Although the world moves at different speeds and any generalization is bound to have some exceptions, there is evidence from research conducted in different places and contexts that educational methods are becoming increasingly digitized and driven by technological innovation. Among the technological trends fueled in many cases by the COVID-19 pandemic and the need to stay at home but online, augmented reality solutions received an additional boost as a valid and versatile educational technology worth exploring and eventually integrating into several teaching methods already in use. Although the technology still faces problems related to affordability, accessibility, and the technical skills required of users, some ongoing projects have already provided evidence that using augmented reality solutions as teaching and learning tools can improve teacher and student learning outcomes by increasing engagement and interactivity. The same issues arose when personal computers, tablets, and smartphones were first discussed as valuable tools for education and have now found their way into most classrooms. This paper reviews some of the key concepts related to augmented reality, as well as some current trends, benefits, and concerns related to its integration into educational contexts in areas such as life sciences, engineering, and health. The work conducted and presented in this paper provides an interesting insight into a technology that has given rise to global phenomena such as Pokémon Go, and continues to improve in terms of portability, usability, and overall user experience. Throughout the paper and in the conclusion section, we discuss the relevance of using the best features of augmented reality and how they can contribute to positive educational outcomes. Full article
(This article belongs to the Special Issue Perception and Interaction in Mixed, Augmented, and Virtual Reality)
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30 pages, 719 KiB  
Article
A Novel JSF-Based Fast Implementation Method for Multiple-Point Multiplication
by Xinze Chen and Yong Fu
Electronics 2023, 12(16), 3530; https://doi.org/10.3390/electronics12163530 - 21 Aug 2023
Viewed by 869
Abstract
ECC is a popular public-key cryptographic algorithm, but it lacks an effective solution to multiple-point multiplication. This paper proposes a novel JSF-based fast implementation method for multiple-point multiplication. The proposed method requires a small storage space and has high performance, making it suitable [...] Read more.
ECC is a popular public-key cryptographic algorithm, but it lacks an effective solution to multiple-point multiplication. This paper proposes a novel JSF-based fast implementation method for multiple-point multiplication. The proposed method requires a small storage space and has high performance, making it suitable for resource-constrained IoT application scenarios. This method stores and encodes the required coordinates in the pre-computation phase and uses table lookup operations to eliminate the conditional judgment operations in JSF-5, which improves the efficiency by about 70% compared to the conventional JSF-5 in generating the sparse form. This paper utilizes Co-Z combined with safegcd to achieve low computational complexity for curve coordinate pre-computation, which further reduces the complexity of multiple-point multiplication in the execution phase of the algorithm. The experiments were performed with two short Weierstrass elliptic curves, nistp256r1 and SM2. In comparison to the various CPU architectures used in the experiments, our proposed method showed an improvement of about 3% over 5-NAF. Full article
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26 pages, 8595 KiB  
Article
An Efficient Bidirectional DC Circuit Breaker Capable of Regenerative Current Breaking for DC Microgrid Application
by Md Mahmudul Hasan, Lo Hai Hiung, Ramani Kannan and S. M. Sanzad Lumen
Electronics 2023, 12(16), 3529; https://doi.org/10.3390/electronics12163529 - 21 Aug 2023
Viewed by 1183
Abstract
The direct current circuit breaker (DCCB) is extensively employed in DC microgrid applications to protect the network during faults. However, numerous DC converters are combined in parallel to form a DC microgrid, which creates a large network inductance. The grid stores energy during [...] Read more.
The direct current circuit breaker (DCCB) is extensively employed in DC microgrid applications to protect the network during faults. However, numerous DC converters are combined in parallel to form a DC microgrid, which creates a large network inductance. The grid stores energy during regular operation, which repels instantaneous current breaking, and this stored energy needs to be eliminated after current breaking. Conventional topologies use different energy absorption methods to dissipate the stored energy after breaking the current. In this paper, an efficient bidirectional DC circuit breaker (EBDCCB) topology is introduced to extract and reuse this energy instead of dissipating it. The proposed topology has bidirectional power flow capability to meet the requirements of DC microgrid applications as energy storage devices are frequently utilized. Furthermore, EBDCCB shows drastically improved performance in terms of current breaking time, voltage stress, regenerated average current, and energy recovery efficiency compared to the conventional DCCB topology. The mathematical modeling and sizing of the components used in the proposed EBDCCB are elaborately analyzed, and detailed performance testing is presented along with extensive PSIM software simulation. Additionally, an experimental investigation is conducted on a laboratory-scale 48 V/1 A prototype. Full article
(This article belongs to the Topic Power System Protection)
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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 1135
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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18 pages, 1031 KiB  
Article
Simple Learning-Based Robust Nonlinear Control of an Electric Pump for Liquid-Propellant Rocket Engines
by Mohammad Jafari, Mahmut Reyhanoglu and Zhandos Kozhabek
Electronics 2023, 12(16), 3527; https://doi.org/10.3390/electronics12163527 - 21 Aug 2023
Viewed by 793
Abstract
This paper presents a robust nonlinear control strategy for an electric pump for liquid-propellant rocket engines. In order to compensate for model uncertainties and disturbances, a gradient-descent-based simple learning control strategy is employed that minimizes the cost function defined on the error dynamics [...] Read more.
This paper presents a robust nonlinear control strategy for an electric pump for liquid-propellant rocket engines. In order to compensate for model uncertainties and disturbances, a gradient-descent-based simple learning control strategy is employed that minimizes the cost function defined on the error dynamics of the nonlinear system. Detailed stability analysis for the nonlinear system is provided. Computer simulation results are included to demonstrate the effectiveness of the nonlinear control method using an electric pump model consisting of a brushless permanent-magnet direct current (DC) motor and a centrifugal pump. In particular, it is shown that by employing the developed nonlinear controller, the mass flow rate can be successfully kept at a certain level, can be changed instantly from one level to another (immediate decrease or increase), or can be changed linearly/nonlinearly, gradually, and continually for a certain period. Full article
(This article belongs to the Section Systems & Control Engineering)
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26 pages, 1146 KiB  
Article
Power Transformer Fault Diagnosis Based on Improved BP Neural Network
by Yongshuang Jin, Hang Wu, Jianfeng Zheng, Ji Zhang and Zhi Liu
Electronics 2023, 12(16), 3526; https://doi.org/10.3390/electronics12163526 - 21 Aug 2023
Cited by 3 | Viewed by 1942
Abstract
Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it [...] Read more.
Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to significant economic losses and social impacts. The traditional detection methods rely on the technical level of power system operation and maintenance personnel, and are based on Dissolved Gas Analysis (DGA) technology, which analyzes the components of dissolved gases in transformer oil for preliminary fault diagnosis. However, with the increasing accuracy and intelligence requirements for transformer fault diagnosis in power grids, the DGA analysis method is no longer able to meet the requirements. Therefore, this article proposes an improved transformer fault diagnosis method based on a residual BP neural network. This method deepens the BP neural network by stacking multiple residual network modules, and fuses and expands gas feature information through an improved BP neural network. In the improved residual BP neural network, SVM is introduced to judge the extracted feature vectors at each layer, screen out feature vectors with high accuracy, and increase their weights. The feature vector with the highest cumulative weight is selected as an input for transformer fault diagnosis. This method utilizes multi-layer neural network mapping to extract gas feature information with more significant feature differences after fusion expansion, thereby effectively improving diagnostic accuracy. The experimental results show that, compared with traditional BP neural network methods, the proposed algorithm has higher accuracy in transformer fault diagnosis, with an accuracy rate of 92%, which can ensure the sustainable, normal, and safe operation of power grids. Full article
(This article belongs to the Special Issue Machine Learning in Power System Monitoring and Control)
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25 pages, 6427 KiB  
Article
HISP: Heterogeneous Image Signal Processor Pipeline Combining Traditional and Deep Learning Algorithms Implemented on FPGA
by Jie Chen, Binghao Wang, Shupei He, Qijun Xing, Xing Su, Wei Liu and Ge Gao
Electronics 2023, 12(16), 3525; https://doi.org/10.3390/electronics12163525 - 21 Aug 2023
Viewed by 1592
Abstract
To tackle the challenges of edge image processing scenarios, we have developed a novel heterogeneous image signal processor (HISP) pipeline combining the advantages of traditional image signal processors and deep learning ISP (DLISP). Through a multi-dimensional image quality assessment (IQA) system integrating deep [...] Read more.
To tackle the challenges of edge image processing scenarios, we have developed a novel heterogeneous image signal processor (HISP) pipeline combining the advantages of traditional image signal processors and deep learning ISP (DLISP). Through a multi-dimensional image quality assessment (IQA) system integrating deep learning and traditional methods like RankIQA, BRISQUE, and SSIM, various partitioning schemes were compared to explore the highest-quality imaging heterogeneous processing scheme. The UNet-specific deep-learning processing unit (DPU) based on a field programmable gate array (FPGA) provided a 14.67× acceleration ratio for the total network and for deconvolution and max pool, the calculation latency was as low as 2.46 ms and 97.10 ms, achieving an impressive speedup ratio of 46.30× and 36.49× with only 4.04 W power consumption. The HISP consisting of a DPU and the FPGA-implemented traditional image signal processor (ISP) submodules, which scored highly in the image quality assessment system, with a single processing time of 524.93 ms and power consumption of only 8.56 W, provided a low-cost and fully replicable solution for edge image processing in extremely low illumination and high noise environments. Full article
(This article belongs to the Section Artificial Intelligence Circuits and Systems (AICAS))
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23 pages, 3602 KiB  
Article
Blockchain and Machine Learning-Based Hybrid IDS to Protect Smart Networks and Preserve Privacy
by Shailendra Mishra
Electronics 2023, 12(16), 3524; https://doi.org/10.3390/electronics12163524 - 21 Aug 2023
Cited by 4 | Viewed by 1753
Abstract
The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide a medium for expression and communication. Malware, phishing, ransomware, and distributed denial-of-service attacks pose a threat to individuals and organisations. To detect and predict cyber threats effectively and accurately, [...] Read more.
The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide a medium for expression and communication. Malware, phishing, ransomware, and distributed denial-of-service attacks pose a threat to individuals and organisations. To detect and predict cyber threats effectively and accurately, an intelligent system must be developed. Cybercriminals can exploit Internet of Things devices and endpoints because they are not intelligent and have limited resources. A hybrid decision tree method (HIDT) is proposed in this article that integrates machine learning with blockchain concepts for anomaly detection. In all datasets, the proposed system (HIDT) predicts attacks in the shortest amount of time and has the highest attack detection accuracy (99.95% for the KD99 dataset and 99.72% for the UNBS-NB 15 dataset). To ensure validity, the binary classification test results are compared to those of earlier studies. The HIDT’s confusion matrix contrasts with previous models by having low FP/FN rates and high TP/TN rates. By detecting malicious nodes instantly, the proposed system reduces routing overhead and has a lower end-to-end delay. Malicious nodes are detected instantly in the network within a short period. Increasing the number of nodes leads to a higher throughput, with the highest throughput measured at 50 nodes. The proposed system performed well in terms of the packet delivery ratio, end-to-end delay, robustness, and scalability, demonstrating the effectiveness of the proposed system. Data can be protected from malicious threats with this system, which can be used by governments and businesses to improve security and resilience. Full article
(This article belongs to the Special Issue AI Security and Safety)
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32 pages, 1797 KiB  
Review
Memristors in Cellular-Automata-Based Computing: A Review
by Rafailia-Eleni Karamani, Iosif-Angelos Fyrigos, Vasileios Ntinas, Ioannis Vourkas, Andrew Adamatzky and Georgios Ch. Sirakoulis
Electronics 2023, 12(16), 3523; https://doi.org/10.3390/electronics12163523 - 20 Aug 2023
Viewed by 2517
Abstract
The development of novel hardware computing systems and methods has been a topic of increased interest for researchers worldwide. New materials, devices, and architectures are being explored as a means to deliver more efficient solutions to contemporary issues. Along with the advancement of [...] Read more.
The development of novel hardware computing systems and methods has been a topic of increased interest for researchers worldwide. New materials, devices, and architectures are being explored as a means to deliver more efficient solutions to contemporary issues. Along with the advancement of technology, there is a continuous increase in methods available to address significant challenges. However, the increased needs to be fulfilled have also led to problems of increasing complexity that require better and faster computing and processing capabilities. Moreover, there is a wide range of problems in several applications that cannot be addressed using the currently available methods and tools. As a consequence, the need for emerging and more efficient computing methods is of utmost importance and constitutes a topic of active research. Among several proposed solutions, we distinguish the development of a novel nanoelectronic device, called a “memristor”, that can be utilized both for storing and processing, and thus it has emerged as a promising circuit element for the design of compact and energy-efficient circuits and systems. The memristor has been proposed for a wide range of applications. However, in this work, we focus on its use in computing architectures based on the concept of Cellular Automata. The combination of the memristor’s performance characteristics with Cellular Automata has boosted further the concept of processing and storing information on the same physical units of a system, which has been extensively studied in the literature as it provides a very good candidate for the implementation of Cellular Automata computing with increased potential and improved characteristics, compared to traditional hardware implementations. In this context, this paper reviews the most recent advancements toward the development of Cellular-Automata-based computing coupled with memristor devices. Several approaches for the design of such novel architectures, called “Memristive Cellular Automata”, exist in the literature. This extensive review provides a thorough insight into the most important developments so far, helping the reader to grasp all the necessary information, which is here presented in an organized and structured manner. Thus, this article aims to pave the way for further development in the field and to bring attention to technological aspects that require further investigation. Full article
(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)
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18 pages, 24055 KiB  
Article
Research on Sliding Mode Control of Dual Active Bridge Converter Based on Linear Extended State Observer in Distributed Electric Propulsion System
by Minsheng Yang and Pengcheng Liu
Electronics 2023, 12(16), 3522; https://doi.org/10.3390/electronics12163522 - 20 Aug 2023
Viewed by 880
Abstract
This paper focuses on the high-performance bidirectional DC-DC converter required in distributed electric propulsion (DEP) systems, with the dual active bridge (DAB) converter chosen as the subject of study. To achieve the goal of stabilizing the output voltage while improving the converter’s anti-interference [...] Read more.
This paper focuses on the high-performance bidirectional DC-DC converter required in distributed electric propulsion (DEP) systems, with the dual active bridge (DAB) converter chosen as the subject of study. To achieve the goal of stabilizing the output voltage while improving the converter’s anti-interference ability and dynamic performance, this paper proposes a novel strategy. In particular, it combines the Linear Extended State Observer (LESO) with a sliding mode control (SMC), proposing a sliding mode control strategy based on the Linear Extended State Observer (LESO-SMC). Notably, this control strategy not only retains the fast dynamic performance of Linear Active Disturbance Rejection Control (LADRC) and the robustness of SMC but also addresses the significant chattering issue inherent in traditional SMC. Comparing the traditional PI, LADRC, and SMC strategies, the results show that when the load changes, the voltage fluctuation of the LESO-SMC strategy proposed in this paper is 0.165 V (0.25 V) in the Matlab/Simulink and RT-Lab platforms, and the average adjustment time is 4 ms (3.5 ms). In contrast, the average voltage fluctuations of PI and LADRC strategies were 3.7 V (4.9 V) and 0.55 V (1.35 V), and the average adjustment times were 99.5 ms (201 ms) and 71.5 ms (77.5 ms), respectively. When the input voltage changes, the proposed LESO-SMC strategy adjusts faster and has almost no voltage fluctuations, while the average voltage fluctuations of the PI and LADRC strategies in the simulation are 0.5 V and 0.1 V, and the average adjustment times are 89.5 ms and 35 ms, and the change in the input voltage in the RT-Lab platform has very little effect on the output voltage. Compared with SMC, the LESO-SMC strategy has no chattering problem. In summary, compared to the other three control strategies, the LESO-SMC strategy proposed in this paper exhibits superior performance in terms of voltage fluctuation and adjustment time during load changes and input voltage changes. It shows a robust anti-interference ability and a rapid dynamic response performance. Full article
(This article belongs to the Section Power Electronics)
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21 pages, 3009 KiB  
Article
A Visually Enhanced Neural Encoder for Synset Induction
by Guang Chen, Fangxiang Feng, Guangwei Zhang, Xiaoxu Li and Ruifan Li
Electronics 2023, 12(16), 3521; https://doi.org/10.3390/electronics12163521 - 20 Aug 2023
Viewed by 676
Abstract
The synset induction task is to automatically cluster semantically identical instances, which are often represented by texts and images. Previous works mainly consider textual parts, while ignoring the visual counterparts. However, how to effectively employ the visual information to enhance the semantic representation [...] Read more.
The synset induction task is to automatically cluster semantically identical instances, which are often represented by texts and images. Previous works mainly consider textual parts, while ignoring the visual counterparts. However, how to effectively employ the visual information to enhance the semantic representation for the synset induction is challenging. In this paper, we propose a Visually Enhanced NeUral Encoder (i.e., VENUE) to learn a multimodal representation for the synset induction task. The key insight lies in how to construct multimodal representations through intra-modal and inter-modal interactions among images and text. Specifically, we first design the visual interaction module through the attention mechanism to capture the correlation among images. To obtain the multi-granularity textual representations, we fuse the pre-trained tags and word embeddings. Second, we design a masking module to filter out weakly relevant visual information. Third, we present a gating module to adaptively regulate the modalities’ contributions to semantics. A triplet loss is adopted to train the VENUE encoder for learning discriminative multimodal representations. Then, we perform clustering algorithms on the obtained representations to induce synsets. To verify our approach, we collect a multimodal dataset, i.e., MMAI-Synset, and conduct extensive experiments. The experimental results demonstrate that our method outperforms strong baselines on three groups of evaluation metrics. Full article
(This article belongs to the Section Artificial Intelligence)
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19 pages, 3281 KiB  
Article
Internal Detection of Ground-Penetrating Radar Images Using YOLOX-s with Modified Backbone
by Xibin Zheng, Sinan Fang, Haitao Chen, Liang Peng and Zhi Ye
Electronics 2023, 12(16), 3520; https://doi.org/10.3390/electronics12163520 - 20 Aug 2023
Viewed by 1012
Abstract
Geological radar is an important method used for detecting internal defects in tunnels. Automatic interpretation techniques can effectively reduce the subjectivity of manual identification, improve recognition accuracy, and increase detection efficiency. This paper proposes an automatic recognition approach for geological radar images (GPR) [...] Read more.
Geological radar is an important method used for detecting internal defects in tunnels. Automatic interpretation techniques can effectively reduce the subjectivity of manual identification, improve recognition accuracy, and increase detection efficiency. This paper proposes an automatic recognition approach for geological radar images (GPR) based on YOLOX-s, aimed at accurately detecting defects and steel arches in any direction. The method utilizes the YOLOX-s neural network and improves the backbone with Swin Transformer to enhance the recognition capability for small targets in geological radar images. To address irregular voids commonly observed in radar images, the CBAM attention mechanism is incorporated to improve the accuracy of detection annotations. We construct a dataset using field detection data that includes targets of different sizes and orientations, representing “voids” and “steel arches”. Our model tackles the challenges of traditional GPR image interpretation and enhances the automatic recognition accuracy and efficiency of radar image detection. In comparative experiments, our improved model achieves a recognition accuracy of 92% for voids and 94% for steel arches, as evaluated on the constructed dataset. Compared to YOLOX-s, the average precision is improved by 6.51%. These results indicate the superiority of our model in geological radar image interpretation. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images)
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18 pages, 3687 KiB  
Article
Research on Earthquake Data Prediction Method Based on DIN–MLP Algorithm
by Zhaoliang An, Guannan Si, Pengxin Tian, Jianxin Li, Xinyu Liang, Fengyu Zhou and Xiaoliang Wang
Electronics 2023, 12(16), 3519; https://doi.org/10.3390/electronics12163519 - 20 Aug 2023
Viewed by 768
Abstract
This paper proposes a recommendation algorithm that combines MLP with the DIN model and conducts simulation experiments in the field of earthquake missing data prediction. The original DIN model may face challenges and weaknesses in earthquake monitoring data prediction, such as a limited [...] Read more.
This paper proposes a recommendation algorithm that combines MLP with the DIN model and conducts simulation experiments in the field of earthquake missing data prediction. The original DIN model may face challenges and weaknesses in earthquake monitoring data prediction, such as a limited capability in handling data loss or anomalies in seismic monitoring stations. To overcome these issues, we innovatively treat seismic monitoring stations as special users and historical data patterns as recommended items. Based on the DIN model, we implement data processing and prediction for seismic monitoring stations and introduce an attention mechanism based on MLP neural networks in the model, while leveraging the prior knowledge base to enhance predictive capabilities. Compared to the original DIN model, our proposed approach not only recommends sequence combinations that meet the demands of seismic monitoring stations but also enhances the matching between station behavior attributes and historical data characteristics, thereby significantly improving prediction accuracy. To validate the effectiveness of our method, we conducted comparative experiments. The results show that the GAUC achieved by the DIN–MLP model reaches 0.69, which is an 11 percent point improvement over the original DIN model. This highlights the remarkable advantages of our algorithm in earthquake missing data prediction. Furthermore, our research reveals the potential of the DIN–MLP algorithm in practical applications, providing more accurate data processing and time series combination recommendations for the field of earthquake monitoring stations, thus contributing to the improvement of monitoring efficiency and accuracy. Full article
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12 pages, 889 KiB  
Article
Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study
by Jihyun Park, Jaeyoung Shin and Byoungju Choi
Electronics 2023, 12(16), 3518; https://doi.org/10.3390/electronics12163518 - 20 Aug 2023
Viewed by 988
Abstract
In software development, early defect detection using static analysis can be performed without executing the source code. However, defects are detected on a non-execution basis, thus resulting in a higher ratio of false positives. Recently, studies have been conducted to effectively perform static [...] Read more.
In software development, early defect detection using static analysis can be performed without executing the source code. However, defects are detected on a non-execution basis, thus resulting in a higher ratio of false positives. Recently, studies have been conducted to effectively perform static analyses using machine learning (ML) and deep learning (DL) technologies. This study examines the techniques for detecting runtime errors used in existing static analysis tools and the causes and rates of false positives. It analyzes the latest static analysis technologies that apply machine learning/deep learning to decrease false positives and compares them with existing technologies in terms of effectiveness and performance. In addition, machine-learning/deep-learning-based defect detection techniques were implemented in experimental environments and real-world software to determine their effectiveness in real-world software. Full article
(This article belongs to the Special Issue Program Slicing and Source Code Analysis: Methods and Applications)
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17 pages, 9298 KiB  
Article
Low-Light Image Contrast Enhancement with Adaptive Noise Attenuator for Augmented Vehicle Detection
by Sungan Yoon and Jeongho Cho
Electronics 2023, 12(16), 3517; https://doi.org/10.3390/electronics12163517 - 20 Aug 2023
Viewed by 873
Abstract
The rapid progress in deep learning technologies has accelerated the use of object detection models, but most models do not operate satisfactorily in low-light environments. As a result, many studies have been conducted on image enhancement techniques aiming to make objects more visible [...] Read more.
The rapid progress in deep learning technologies has accelerated the use of object detection models, but most models do not operate satisfactorily in low-light environments. As a result, many studies have been conducted on image enhancement techniques aiming to make objects more visible by increasing contrast, but the process of image enhancement may negatively impact detection as it further strengthens unwanted noises due to indirect factors of light reflection such as overall low brightness, streetlamps, and neon signboards. Therefore, in this study, we propose a technique for improving the performance of object detection in low-light environments. The proposed technique inverts a low-light image to make it similar to a hazy image and then uses a haze removal algorithm based on entropy and fidelity to increase image contrast, clarifying the boundary between the object and the background. In the next step, we used the adaptive 2D Wiener filter (A2WF) to attenuate the noise accidentally strengthened during the image enhancement process and reinforced the boundary between the object and the background to increase detection performance. The test evaluation results showed that the proposed image enhancement scheme significantly increased image perception performance with the perception-based image quality evaluator being 12.73% lower than existing image enhancement techniques. In a comparison of vehicle detection performance, the proposed technique for enhancing nighttime images combined with the detection model proved its effectiveness by increasing the average precision by up to 18.63% against existing detection methods. Full article
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13 pages, 1724 KiB  
Article
Context-Dependent Multimodal Sentiment Analysis Based on a Complex Attention Mechanism
by Lujuan Deng, Boyi Liu, Zuhe Li, Jiangtao Ma and Hanbing Li
Electronics 2023, 12(16), 3516; https://doi.org/10.3390/electronics12163516 - 20 Aug 2023
Cited by 1 | Viewed by 1224
Abstract
Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article [...] Read more.
Multimodal sentiment analysis aims to understand people’s attitudes and opinions from different data forms. Traditional modality fusion methods for multimodal sentiment analysis con-catenate or multiply various modalities without fully utilizing context information and the correlation between modalities. To solve this problem, this article provides a new model based on a multimodal sentiment analysis framework based on a recurrent neural network with a complex attention mechanism. First, after the raw data is preprocessed, the numerical feature representation is obtained using feature extraction. Next, the numerical features are input into the recurrent neural network, and the output results are multimodally fused using a complex attention mechanism layer. The objective of the complex attention mechanism is to leverage enhanced non-linearity to more effectively capture the inter-modal correlations, thereby improving the performance of multimodal sentiment analysis. Finally, the processed results are fed into the classification layer and the sentiment output is obtained using the classification layer. This process can effectively capture the semantic information and contextual relationship of the input sequence and fuse different pieces of modal information. Our model was tested on the CMU-MOSEI datasets, achieving an accuracy of 82.04%. Full article
(This article belongs to the Special Issue Natural Language Processing Method: Deep Learning and Deep Semantics)
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16 pages, 5994 KiB  
Article
Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer
by Fengyun Xie, Gan Wang, Haiyan Zhu, Enguang Sun, Qiuyang Fan and Yang Wang
Electronics 2023, 12(16), 3515; https://doi.org/10.3390/electronics12163515 - 19 Aug 2023
Cited by 2 | Viewed by 969
Abstract
Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault diagnosis method based on SVD-GST combined with [...] Read more.
Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault diagnosis method based on SVD-GST combined with the Vision Transformer. Firstly, the one-dimensional vibration signal is preprocessed to reduce noise using singular value decomposition (SVD) to obtain a more accurate and useful signal. Then, the generalized S-transform (GST) is used to convert the processed one-dimensional vibration signal into a two-dimensional time–frequency image and make full use of the advantages of deep learning in image classification with higher recognition accuracy. In order to avoid the problem of limited sensory fields in CNN and the need for an RNN to compute step by step over time when processing sequence data, the use of a Vision Transformer model for pattern recognition classification is proposed. Finally, an experimental platform for the fault diagnosis of rolling bearings is built. The model is experimentally validated, achieving an average accuracy of 98.52% over multiple tests. Additionally, compared with the SVD-GST-2DCNN, STFT-CNN-LSTM, SVD-GST-LSTM, and GST-ViT fault diagnosis models, the proposed method has higher diagnostic accuracy and stability, providing a new method for rolling bearing fault diagnosis. Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
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23 pages, 10838 KiB  
Article
A Period Energy Method for Demagnetization Detection in Surface Permanent Magnet Motors with Search Coils
by Wen Huang, Junquan Chen, Wu Su, Haitao Liu, Ke Lv and Jinghua Hu
Electronics 2023, 12(16), 3514; https://doi.org/10.3390/electronics12163514 - 19 Aug 2023
Viewed by 771
Abstract
Irreversible demagnetization of permanent magnets (PMs) in PM synchronous motors (PMSMs) degrades the performance and efficiency of a machine and its drive system. There are numerous fault diagnosis methods for detecting demagnetization under steady-state conditions. However, only a few works could be found [...] Read more.
Irreversible demagnetization of permanent magnets (PMs) in PM synchronous motors (PMSMs) degrades the performance and efficiency of a machine and its drive system. There are numerous fault diagnosis methods for detecting demagnetization under steady-state conditions. However, only a few works could be found on fault diagnosis under dynamic conditions, whereas the dynamic operation of a motor is a very common scenario, e.g., electric vehicles. The voltage and current signal-based traditional fault detection method is not only affected by the structure of the motor, but it also becomes complicated to extract signals involving fault characteristics. Hence, this paper proposes a search coil-based online method for detecting demagnetization faults in PMSMs under dynamic conditions, which are not affected by the motor structure. To gather the flux of the stator tooth, flexible Printed circuit board (FPCB) search coils are positioned at the stator slot. The search coil is made up of two branches that are one pole apart and arranged in reverse sequence. In this installation option, the output signal in the fault state cannot be eliminated, and the output signal in the health state is zero. This paper defines only that characteristic value related to the position angle of the rotor. Further, the aim was to simultaneously eliminate the influence of elements like the search coil installation error and the inherent dispersion of the permanent magnet on the detection results. To characterize the fault degree, the measurement differential between the health state and the fault state is further integrated according to a predetermined angle range. Last but not least, speed-independent detection of individual permanent magnet demagnetization faults is possible using rotor position and stator tooth flux. A six-phase PMSM was used in experiments to show the efficiency of the suggested approach. The findings of the experiment demonstrate that the suggested strategy may precisely ascertain when a defect will occur. Full article
(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Control in Industrial Electronics)
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22 pages, 38901 KiB  
Article
Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers
by Jieun Lee, Tae-yong Kim, Seunghyo Beak, Yeeun Moon and Jongpil Jeong
Electronics 2023, 12(16), 3513; https://doi.org/10.3390/electronics12163513 - 19 Aug 2023
Cited by 1 | Viewed by 1897
Abstract
The present study proposes a Real-Time Pose Estimation technique using OpenPose based on ResNet-50 that enables rapid safety prevention and accident detection among field workers. Field workers perform tasks in high-risk environments, and accurate Pose Estimation is a crucial aspect of ensuring worker [...] Read more.
The present study proposes a Real-Time Pose Estimation technique using OpenPose based on ResNet-50 that enables rapid safety prevention and accident detection among field workers. Field workers perform tasks in high-risk environments, and accurate Pose Estimation is a crucial aspect of ensuring worker safety. However, it is difficult for Real-Time Pose Estimation to be conducted in such a way as to simultaneously meet Real-Time processing requirements and accuracy in complex environments. To address these issues, the current study uses the OpenPose algorithm based on ResNet-50, which is a neural network architecture that performs well in both image classification and feature extraction tasks, thus providing high accuracy and efficiency. OpenPose is an algorithm specialized for multi-human Pose Estimation that can be used to estimate the body structure and joint positions of a large number of individuals in real time. Here, we train ResNet-50-based OpenPose for Real-Time Pose Estimation and evaluate it on various datasets, including actions performed by real field workers. The experimental results show that the proposed algorithm achieves high accuracy in the Real-Time Pose Estimation of field workers. It also provides stable results while maintaining a fast image processing speed, thus confirming its applicability in real field environments. Full article
(This article belongs to the Special Issue Image Segmentation)
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9 pages, 4004 KiB  
Communication
A Novel Unit Classification Method for Fast and Accurate Calculation of Radiation Patterns
by Hao Zhou, Jiren Li and Kun Wei
Electronics 2023, 12(16), 3512; https://doi.org/10.3390/electronics12163512 - 19 Aug 2023
Viewed by 824
Abstract
This paper proposes a novel unit classification technique to enhance the accuracy of the conventional pattern multiplication method by taking the mutual coupling effect and edge effect into consideration. The proposed technique classifies antenna elements into different groups based on their positions in [...] Read more.
This paper proposes a novel unit classification technique to enhance the accuracy of the conventional pattern multiplication method by taking the mutual coupling effect and edge effect into consideration. The proposed technique classifies antenna elements into different groups based on their positions in arrays, specifically corner, edge, and inner groups. By simulating the radiation patterns of antenna elements with different boundary conditions, the pattern multiplication method is then used to calculate the radiation pattern of the antenna array based on the simulated results. Several numerical examples, including a square array, a hexagonal array, and a phased array, are provided to validate the effectiveness of the proposed method. The numerical results demonstrate that the proposed method not only reduces the computational time and memory usage but also significantly improves the accuracy. The proposed method provides a powerful tool for synthesizing and predicting the radiation pattern of array antennas and offers new avenues for optimizing array antennas and phased array antennas. Full article
(This article belongs to the Special Issue Antenna Design and Its Applications)
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12 pages, 2681 KiB  
Article
Design of a Four-Wheel Steering Mobile Robot Platform and Adaptive Steering Control for Manual Operation
by Beomsu Bae and Dong-Hyun Lee
Electronics 2023, 12(16), 3511; https://doi.org/10.3390/electronics12163511 - 19 Aug 2023
Cited by 2 | Viewed by 3227
Abstract
The recent advancementsin autonomous driving technology have led to an increased utilization of mobile robots across various industries. Notably, four-wheel steering robots have gained significant attention due to their robustness and agile maneuvering capabilities. This paper presents a novel four-wheel steering robot platform [...] Read more.
The recent advancementsin autonomous driving technology have led to an increased utilization of mobile robots across various industries. Notably, four-wheel steering robots have gained significant attention due to their robustness and agile maneuvering capabilities. This paper presents a novel four-wheel steering robot platform for research purposes and an adaptive four-wheel steering control algorithm for efficient manual operation. The proposed robot platform is specifically designed as a simple and compact research-oriented platform for developing navigation and manual operation of four-wheel steering robots. The compact design of the robot platform allows for additional space utilization, while the horizontal independent steering system provides precise control and enhanced maneuverability. The adaptive four-wheel steering control algorithm aims to offer efficient and intuitive manual operation of the four-wheel steering robot, aligning with the intentions of the human operator. It enables the platform to utilize front-wheel steering under normal circumstances and efficiently reduce the turning radius by employing rear wheel steering when additional steering input is required. Experimental results demonstrated the accurate steering performance of the robot platform and effectiveness of the adaptive steering algorithm. The developed four-wheel steering robot platform and the adaptive steering control algorithm serve as valuable tools for further research and development in the fields of autonomous driving and steering algorithms. Full article
(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)
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22 pages, 10449 KiB  
Article
An Intelligent Detection Method for Approach Distances of Large Construction Equipment in Substations
by Leixiong Wang, Bo Wang, Jiaxin Zhang, Hengrui Ma, Peng Luo and Tianrui Yin
Electronics 2023, 12(16), 3510; https://doi.org/10.3390/electronics12163510 - 19 Aug 2023
Viewed by 954
Abstract
The safe approach distance detection of large construction equipment in substations is important to ensure the safety and stability of the power system, as well as to prevent equipment damage, power outages and other accidents. The current method is unable to intelligently distinguish [...] Read more.
The safe approach distance detection of large construction equipment in substations is important to ensure the safety and stability of the power system, as well as to prevent equipment damage, power outages and other accidents. The current method is unable to intelligently distinguish construction equipment from power equipment and realize real-time safety approach distance detection. Therefore, this paper constructs a safety approach distance detection system for large-scale construction equipment in substations based on stereo vision and target detection, and realizes real-time high-precision safe approach distance detection between large-scale construction equipment and electric power equipment. Firstly, the system distinguishes construction equipment from power equipment using a GhostNet-based substation construction target detection model. Secondly, the system obtains spatial information regarding the operation scene using a lightweight stereo matching model based on channel attention, then calculates the spatial surface center of the target based on the spatial information and detection results, and finally calculates the safety approach distance between construction equipment and power equipment. Compared with MobileNetv3-YOLOv4, the map and the recall rate of the proposed method are improved by 13.1% and 29.0%; compared with the AnyNet stereo matching method, the proposed method decreases the end point error and 3 pixels error by 34.2% and 25.8%. The actual data show that the detection speed of the proposed method is 19.35 frames per second, and the mean absolute error is 0.942 m and the mean relative error is 3.802%. This method can accurately measure the safe approach distance in real time in real scenarios to guarantee the safety of personnel and equipment. Full article
(This article belongs to the Section Artificial Intelligence)
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4 pages, 179 KiB  
Editorial
Recent Advances in the Use of eXplainable Artificial Intelligence Techniques for Wind Turbine Systems Condition Monitoring
by Davide Astolfi, Fabrizio De Caro and Alfredo Vaccaro
Electronics 2023, 12(16), 3509; https://doi.org/10.3390/electronics12163509 - 18 Aug 2023
Cited by 1 | Viewed by 910
Abstract
There is a good probability that wind turbines will emerge as one of the predominant technologies for electricity production in the upcoming decades [...] Full article
(This article belongs to the Special Issue Advances in Data-Driven Wind Turbine Condition Monitoring)
14 pages, 2126 KiB  
Article
Optimized Feature Extraction for Sample Efficient Deep Reinforcement Learning
by Yuangang Li, Tao Guo, Qinghua Li and Xinyue Liu
Electronics 2023, 12(16), 3508; https://doi.org/10.3390/electronics12163508 - 18 Aug 2023
Viewed by 781
Abstract
In deep reinforcement learning, agent exploration still has certain limitations, while low efficiency exploration further leads to the problem of low sample efficiency. In order to solve the exploration dilemma caused by white noise interference and the separation derailment problem in the environment, [...] Read more.
In deep reinforcement learning, agent exploration still has certain limitations, while low efficiency exploration further leads to the problem of low sample efficiency. In order to solve the exploration dilemma caused by white noise interference and the separation derailment problem in the environment, we present an innovative approach by introducing an intricately honed feature extraction module to harness the predictive errors, generate intrinsic rewards, and use an ancillary agent training paradigm that effectively solves the above problems and significantly enhances the agent’s capacity for comprehensive exploration within environments characterized by sparse reward distribution. The efficacy of the optimized feature extraction module is substantiated through comparative experiments conducted within the arduous exploration problem scenarios often employed in reinforcement learning investigations. Furthermore, a comprehensive performance analysis of our method is executed within the esteemed Atari 2600 experimental setting, yielding noteworthy advancements in performance and showcasing the attainment of superior outcomes in six selected experimental environments. Full article
(This article belongs to the Special Issue Trends and Prospects in Machine Learning Technologies: Deep Learning, Reinforcement Learning and Q-learning)
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21 pages, 3462 KiB  
Article
DoseFormer: Dynamic Graph Transformer for Postoperative Pain Prediction
by Cao Zhang, Xiaohui Zhao, Ziyi Zhou, Xingyuan Liang and Shuai Wang
Electronics 2023, 12(16), 3507; https://doi.org/10.3390/electronics12163507 - 18 Aug 2023
Viewed by 778
Abstract
Many patients suffer from postoperative pain after surgery, which causes discomfort and influences recovery after the operation. During surgery, the anesthetists usually rely on their own experience when anesthetizing, which is not stable for avoiding postoperative pain. Hence, it is essential to predict [...] Read more.
Many patients suffer from postoperative pain after surgery, which causes discomfort and influences recovery after the operation. During surgery, the anesthetists usually rely on their own experience when anesthetizing, which is not stable for avoiding postoperative pain. Hence, it is essential to predict postoperative pain and give proper doses accordingly. Recently, the relevance of various clinical parameters and nociception has been investigated in many works, and several indices have been proposed for measuring the level of nociception. However, expensive advanced equipment is required when applying advanced medical technologies, which is not accessible to most institutions. In our work, we propose a deep learning model based on a dynamic graph transformer framework named DoseFormer to predict postoperative pain in a short period after an operation utilizing dynamic patient data recorded in existing widely utilized equipment (e.g., anesthesia monitor). DoseFormer consists of two modules: (i) We design a temporal model utilizing a long short-term memory (LSTM) model with an attention mechanism to capture dynamic intraoperative data of the patient and output a hybrid semantic embedding representing the patient information. (ii) We design a graph transformer network (GTN) to infer the postoperative pain level utilizing the relations across the patient embeddings. We evaluate the DoseFormer system with the medical records of over 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The experimental results show that our model achieves 92.16% accuracy for postoperative pain prediction and has a better comprehensive performance compared with baselines. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence in Healthcare and Biomedical Systems)
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13 pages, 4023 KiB  
Article
Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost
by Tingxia Li, Tiankai Li, Rina Su, Jile Xin and Ding Han
Electronics 2023, 12(16), 3506; https://doi.org/10.3390/electronics12163506 - 18 Aug 2023
Viewed by 864
Abstract
Aiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research object, a [...] Read more.
Aiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research object, a method based on social learning (SL) is proposed using the Whale Optimization Algorithm (WOA) and XGBoost for goat motion behavior recognition. In this method, the XGBoost parameters are optimized by the WOA combined with social learning strategies to improve the classification and recognition accuracy. The results show that the recognition rate of lying behavior was as high as 97.14%, and the average recognition rate of the four movement behaviors was 94.42%, meeting the requirements of goat motion behavior recognition. Compared with the conventional XGBoost algorithm, the average recognition rate was increased by 3.41% and the recognition accuracy was improved. The results of this study can provide a reference for goat health assessment and intelligent disease warning. Full article
(This article belongs to the Special Issue Applications of Deep Learning Techniques)
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