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Electronics, Volume 12, Issue 21 (November-1 2023) – 172 articles

Cover Story (view full-size image): The presented research aimed to determine the security of RFID systems in relation to electromagnetic information leakage. The possibilities of the hardware security of data contained on media, such as the encryption or coding of these data, have not been the subject of research. Comprehensive analyses and experiments were aimed primarily at determining the possibility of carrying out the electromagnetic infiltration process of RFID tags and also commercial systems. For this purpose, a number of experiments were carried out using additional elements, such as passive and active filters or amplifiers, which were aimed at increasing the range of communication with the identifier to values enabling unauthorized access to data. View this paper
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14 pages, 437 KiB  
Article
Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting
by Yuefang Gao, Yiteng Cai, Xuanming Bi, Bizheng Li, Shunpeng Li and Weiping Zheng
Electronics 2023, 12(21), 4553; https://doi.org/10.3390/electronics12214553 - 6 Nov 2023
Cited by 1 | Viewed by 1112
Abstract
Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target [...] Read more.
Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target domains. Existing methods primarily emphasize achieving domain-invariant features through global feature adaptation, often neglecting the potential benefits of transferable local features across different domains. To address this issue, we propose a novel framework for CD-FER that combines reliable global–local representation learning and dynamic label weighting. Our framework incorporates two key modules: the Pseudo-Complementary Label Generation (PCLG) module, which leverages pseudo-labels and complementary labels obtained using a credibility threshold to learn domain-invariant global and local features, and the Label Dynamic Weight Matching (LDWM) module, which assesses the learning difficulty of each category and adaptively assigns corresponding label weights, thereby enhancing the classification performance in the target domain. We evaluate our approach through extensive experiments and analyses on multiple public datasets, including RAF-DB, FER2013, CK+, JAFFE, SFW2.0, and ExpW. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods, with an average accuracy improvement of 3.5% across the five datasets. Full article
(This article belongs to the Section Artificial Intelligence)
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13 pages, 1764 KiB  
Article
Fast Health State Estimation of Lead–Acid Batteries Based on Multi-Time Constant Current Charging Curve
by Chengti Huang and Na Li
Electronics 2023, 12(21), 4552; https://doi.org/10.3390/electronics12214552 - 6 Nov 2023
Viewed by 1111
Abstract
Lead–acid batteries are widely used, and their health status estimation is very important. To address the issues of low fitting accuracy and inaccurate prediction of traditional lead–acid battery health estimation, a battery health estimation model is proposed that relies on charging curve analysis [...] Read more.
Lead–acid batteries are widely used, and their health status estimation is very important. To address the issues of low fitting accuracy and inaccurate prediction of traditional lead–acid battery health estimation, a battery health estimation model is proposed that relies on charging curve analysis using historical degradation data. This model does not require the assistance of battery mechanism models or empirical degradation models, instead, it is combined with improved deep learning algorithms. A long short-term memory (LSTM) regression model was established, and parameter optimization was performed using the bat algorithm (BA). The experimental results show that the proposed model can achieve an accurate capacity estimation of lead–acid batteries. Full article
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23 pages, 636 KiB  
Article
Forecasting Significant Stock Market Price Changes Using Machine Learning: Extra Trees Classifier Leads
by Antonio Pagliaro
Electronics 2023, 12(21), 4551; https://doi.org/10.3390/electronics12214551 - 6 Nov 2023
Cited by 2 | Viewed by 2276
Abstract
Predicting stock market fluctuations is a difficult task due to its intricate and ever-changing nature. To address this challenge, we propose an approach to minimize forecasting errors by utilizing a classification-based technique, which is a widely used set of algorithms in the field [...] Read more.
Predicting stock market fluctuations is a difficult task due to its intricate and ever-changing nature. To address this challenge, we propose an approach to minimize forecasting errors by utilizing a classification-based technique, which is a widely used set of algorithms in the field of machine learning. Our study focuses on the potential effectiveness of this approach in improving stock market predictions. Specifically, we introduce a new method to predict stock returns using an Extra Trees Classifier. Technical indicators are used as inputs to train our model while the target is the percentage difference between the closing price and the closing price after 10 trading days for 120 companies from various industries. The 10-day time frame strikes a good balance between accuracy and practicality for traders, avoiding the low accuracy of short time frames and the impracticality of longer ones. The Extra Trees Classifier algorithm is ideal for stock market predictions because of its ability to handle large data sets with a high number of input features and improve model robustness by reducing overfitting. Our results show that our Extra Trees Classifier model outperforms the more traditional Random Forest method, achieving an accuracy of 86.1%. These findings suggest that our model can effectively predict significant price changes in the stock market with high precision. Overall, our study provides valuable insights into the potential of classification-based techniques in enhancing stock market predictions. Full article
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13 pages, 414 KiB  
Article
Human Body as a Signal Transmission Medium for Body-Coupled Communication: Galvanic-Mode Models
by Vladimir Aristov and Atis Elsts
Electronics 2023, 12(21), 4550; https://doi.org/10.3390/electronics12214550 - 6 Nov 2023
Viewed by 1217
Abstract
Signal propagation models play a fundamental role in radio frequency communication research. However, emerging communication methods, such as body-coupled communication (BCC), require the creation of new models. In this paper, we introduce mathematical models that approximate the human body as an electrical circuit, [...] Read more.
Signal propagation models play a fundamental role in radio frequency communication research. However, emerging communication methods, such as body-coupled communication (BCC), require the creation of new models. In this paper, we introduce mathematical models that approximate the human body as an electrical circuit, as well as linear regression- and random forest-based predictive models that infer the expected signal loss from its frequency, measurement point locations, and body parameters. The results demonstrate a close correspondence between the amplitude-frequency response (AFR) predicted by the electrical circuit models and the experimental data gathered from volunteers. The accuracy of our predictive models was assessed by using their root mean square errors (RMSE), ranging from 1.5 to 7 dB depending on the signal frequency within the 0.05 to 20 MHz range. These results allow researchers and engineers to simulate and forecast the expected signal loss within BCC systems during their design phase. Full article
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15 pages, 1059 KiB  
Article
Fault-Tolerant Safety-Critical Control for Nonlinear Affine System by Using High-Order Control Barrier Function
by Zhe Dong, Jianning Li and Hailun Wang
Electronics 2023, 12(21), 4549; https://doi.org/10.3390/electronics12214549 - 6 Nov 2023
Viewed by 843
Abstract
A class of fault-tolerant safety-critical controller design methods based on high-order control barrier function (HOCBF) is proposed to address the problem of safety and stability of system affected by actuator faults in safety-constrained nonlinear affine system. Firstly, the fault information is incorporated into [...] Read more.
A class of fault-tolerant safety-critical controller design methods based on high-order control barrier function (HOCBF) is proposed to address the problem of safety and stability of system affected by actuator faults in safety-constrained nonlinear affine system. Firstly, the fault information is incorporated into the conventional HOCBF to obtain a new type of HOCBF with faults. Secondly, in the case of a strictly required range of control inputs, a sufficient condition is proposed to satisfy the existing constraints, where the control inputs are always within the restricted range and the sufficient condition is expressed as feasibility constraints. Next, based on the quadratic programming (QP) method, the control Lyapunov function, fault HOCBF, and feasibility constraints are incorporated together to ensure that the overall feasibility, stabilization, and safety are guaranteed of the considered closed-loop system. Finally, the adaptive cruise control system is taken as an example to verify the effectiveness of the proposed method. Full article
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18 pages, 6055 KiB  
Article
Optimized Trajectory Tracking for Robot Manipulators with Uncertain Dynamics: A Composite Position Predictive Control Approach
by Shanrong Ren, Linyan Han, Jianliang Mao and Jun Li
Electronics 2023, 12(21), 4548; https://doi.org/10.3390/electronics12214548 - 6 Nov 2023
Viewed by 1109
Abstract
This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we [...] Read more.
This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively. Full article
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23 pages, 1650 KiB  
Article
Resolving Agent Conflicts Using Enhanced Uncertainty Modeling Tools for Intelligent Decision Making
by Yanhui Zhai, Zihan Jia and Deyu Li
Electronics 2023, 12(21), 4547; https://doi.org/10.3390/electronics12214547 - 5 Nov 2023
Cited by 1 | Viewed by 854
Abstract
Conflict analysis in intelligent decision making has received increasing attention in recent years. However, few researchers have analyzed conflicts by considering trustworthiness from the perspective of common agreement and common opposition. Since L-fuzzy three-way concept lattice is able to describe both the [...] Read more.
Conflict analysis in intelligent decision making has received increasing attention in recent years. However, few researchers have analyzed conflicts by considering trustworthiness from the perspective of common agreement and common opposition. Since L-fuzzy three-way concept lattice is able to describe both the attributes that objects commonly possess and the attributes that objects commonly do not possess, this paper introduces an L-fuzzy three-way concept lattice to capture the issues on which agents commonly agree and the issues which they commonly oppose, and proposes a hybrid conflict analysis model. In order to resolve conflicts identified by the proposed model, we formulate the problem as a knapsack problem and propose a method for selecting the optimal attitude change strategy. This strategy takes into account the associated costs and aims to provide the decision maker with the most favorable decision in terms of resolving conflicts and reaching consensus. To validate the effectiveness and feasibility of the proposed model, a case study is conducted, providing evidence of the model’s efficacy and viability in resolving conflicts. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications, Volume II)
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16 pages, 4180 KiB  
Article
Pixel-Level Degradation for Text Image Super-Resolution and Recognition
by Xiaohong Qian, Lifeng Xie, Ning Ye, Renlong Le and Shengying Yang
Electronics 2023, 12(21), 4546; https://doi.org/10.3390/electronics12214546 - 5 Nov 2023
Viewed by 1095
Abstract
In the realm of image reconstruction, deep learning-based super-resolution (SR) has established itself as a prevalent technique, particularly in the domain of text image restoration. This study aims to address notable deficiencies in existing research, including constraints imposed by restricted datasets and challenges [...] Read more.
In the realm of image reconstruction, deep learning-based super-resolution (SR) has established itself as a prevalent technique, particularly in the domain of text image restoration. This study aims to address notable deficiencies in existing research, including constraints imposed by restricted datasets and challenges related to model generalization. Specifically, the goal is to enhance the super-resolution network’s reconstruction of scene text image super-resolution and utilize the generated degenerate dataset to alleviate issues associated with poor generalization due to the sparse scene text image super-resolution dataset. The methodology employed begins with the degradation of images from the MJSynth dataset, using a stochastic degradation process to create eight distinct degraded versions. Subsequently, a blank image is constructed, preserving identical dimensions to the low-resolution image, with each pixel sourced randomly from the corresponding points across the eight degraded images. Following several iterations of training via Finetune, the LR-HR method is applied to the TextZoom dataset. The pivotal metric for assessment is optical character recognition (OCR) accuracy, recognized for its fundamental role in gauging the pragmatic effectiveness of this approach. The experimental findings reveal a notable enhancement in OCR accuracy when compared to the TBSRN model, yielding improvements of 2.4%, 2.3%, and 4.8% on the TextZoom dataset. This innovative approach, founded on pixel-level degradation, not only exhibits commendable generalization capabilities but also demonstrates resilience in confronting the intricate challenges inherent to text image super-resolution. Full article
(This article belongs to the Special Issue Deep Learning in Image Processing and Pattern Recognition)
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26 pages, 1568 KiB  
Systematic Review
A Systematic Review on Deep-Learning-Based Phishing Email Detection
by Kutub Thakur, Md Liakat Ali, Muath A. Obaidat and Abu Kamruzzaman
Electronics 2023, 12(21), 4545; https://doi.org/10.3390/electronics12214545 - 5 Nov 2023
Cited by 2 | Viewed by 6139
Abstract
Phishing attacks are a growing concern for individuals and organizations alike, with the potential to cause significant financial and reputational damage. Traditional methods for detecting phishing attacks, such as blacklists and signature-based techniques, have limitations that have led to developing more advanced techniques. [...] Read more.
Phishing attacks are a growing concern for individuals and organizations alike, with the potential to cause significant financial and reputational damage. Traditional methods for detecting phishing attacks, such as blacklists and signature-based techniques, have limitations that have led to developing more advanced techniques. In recent years, machine learning and deep learning techniques have gained attention for their potential to improve the accuracy of phishing detection. Deep learning algorithms, such as CNNs and LSTMs, are designed to learn from patterns and identify anomalies in data, making them more effective in detecting sophisticated phishing attempts. To develop a comprehensive understanding of the current state of research on the use of deep learning techniques for phishing detection, a systematic literature review is necessary. This review aims to identify the various deep learning techniques used for phishing detection, their effectiveness, and areas for future research. By synthesizing the findings of relevant studies, this review identifies the strengths and limitations of different approaches and provides insights into the challenges that need to be addressed to improve the accuracy and effectiveness of phishing detection. This review aims to contribute to developing a coherent and evidence-based understanding of the use of deep learning techniques for phishing detection. The review identifies gaps in the literature and informs the development of future research questions and areas of focus. With the increasing sophistication of phishing attacks, applying deep learning in this area is a critical and rapidly evolving field. This systematic literature review aims to provide insights into the current state of research and identify areas for future research to advance the field of phishing detection using deep learning. Full article
(This article belongs to the Special Issue Cyber-Security in Smart Cities: Challenges and Solution)
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18 pages, 1146 KiB  
Article
BioEdge: Accelerating Object Detection in Bioimages with Edge-Based Distributed Inference
by Hyunho Ahn, Munkyu Lee, Sihoon Seong, Minhyeok Lee, Gap-Joo Na, In-Geol Chun, Youngpil Kim and Cheol-Ho Hong
Electronics 2023, 12(21), 4544; https://doi.org/10.3390/electronics12214544 - 5 Nov 2023
Cited by 1 | Viewed by 1183
Abstract
Convolutional neural networks (CNNs) have enabled effective object detection tasks in bioimages. Unfortunately, implementing such an object detection model can be computationally intensive, especially on resource-limited hardware in a laboratory or hospital setting. This study aims to develop a framework called BioEdge that [...] Read more.
Convolutional neural networks (CNNs) have enabled effective object detection tasks in bioimages. Unfortunately, implementing such an object detection model can be computationally intensive, especially on resource-limited hardware in a laboratory or hospital setting. This study aims to develop a framework called BioEdge that can accelerate object detection using Scaled-YOLOv4 and YOLOv7 by leveraging edge computing for bioimage analysis. BioEdge employs a distributed inference technique with Scaled-YOLOv4 and YOLOv7 to harness the computational resources of both a local computer and an edge server, enabling rapid detection of COVID-19 abnormalities in chest radiographs. By implementing distributed inference techniques, BioEdge addresses privacy concerns that can arise when transmitting biomedical data to an edge server. Additionally, it incorporates a computationally lightweight autoencoder at the split point to reduce data transmission overhead. For evaluation, this study utilizes the COVID-19 dataset provided by the Society for Imaging Informatics in Medicine (SIIM). BioEdge is shown to improve the inference latency of Scaled-YOLOv4 and YOLOv7 by up to 6.28 times with negligible accuracy loss compared to local computer execution in our evaluation setting. Full article
(This article belongs to the Special Issue Application Research Using AI, IoT, HCI, and Big Data Technologies)
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24 pages, 3313 KiB  
Article
An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning
by Washington Enyinna Mbonu, Carsten Maple and Gregory Epiphaniou
Electronics 2023, 12(21), 4543; https://doi.org/10.3390/electronics12214543 - 5 Nov 2023
Cited by 1 | Viewed by 1263
Abstract
Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global model through the local training of multiple edge devices. It uses a central server for model communication and the aggregation of post-trained models. The central server orchestrates the training [...] Read more.
Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global model through the local training of multiple edge devices. It uses a central server for model communication and the aggregation of post-trained models. The central server orchestrates the training process by sending each participating device an initial or pre-trained model for training. To achieve the learning objective, focused updates from edge devices are sent back to the central server for aggregation. While such an architecture and information flows can support the preservation of the privacy of participating device data, the strong dependence on the central server is a significant drawback of this framework. Having a central server could potentially lead to a single point of failure. Further, a malicious server may be able to successfully reconstruct the original data, which could impact on trust, transparency, fairness, privacy, and security. Decentralizing the FL process can successfully address these issues. Integrating a decentralized protocol such as Blockchain technology into Federated Learning techniques will help to address these issues and ensure secure aggregation. This paper proposes a Blockchain-based secure aggregation strategy for FL. Blockchain is implemented as a channel of communication between the central server and edge devices. It provides a mechanism of masking device local data for secure aggregation to prevent compromise and reconstruction of the training data by a malicious server. It enhances the scalability of the system, eliminates the threat of a single point of failure of the central server, reduces vulnerability in the system, ensures security, and transparent communication. Furthermore, our framework utilizes a fault-tolerant server to assist in handling dropouts and stragglers which can occur in federated environments. To reduce the training time, we synchronously implemented a callback or end-process mechanism once sufficient post-trained models have been returned for aggregation (threshold accuracy achieved). This mechanism resynchronizes clients with a stale and outdated model, minimizes the wastage of resources, and increases the rate of convergence of the global model. Full article
(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)
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26 pages, 5326 KiB  
Article
Adaptive Scalable Video Streaming (ASViS): An Advanced ABR Transmission Protocol for Optimal Video Quality
by Eliecer Peña-Ancavil, Claudio Estevez, Andrés Sanhueza and Marcos Orchard
Electronics 2023, 12(21), 4542; https://doi.org/10.3390/electronics12214542 - 4 Nov 2023
Viewed by 1218
Abstract
Multimedia video streaming, identified as the dominant internet data consumption service, brings forth challenges in consistently delivering optimal video quality. Dynamic Adaptive Streaming over HTTP (DASH), while prevalent, often encounters buffering problems, causing video pauses due to empty video buffers. This study introduces [...] Read more.
Multimedia video streaming, identified as the dominant internet data consumption service, brings forth challenges in consistently delivering optimal video quality. Dynamic Adaptive Streaming over HTTP (DASH), while prevalent, often encounters buffering problems, causing video pauses due to empty video buffers. This study introduces the Adaptive Scalable Video Streaming (ASViS) protocol as a solution. ASViS incorporates scalable video coding, a flow-controlled User Datagram Protocol (UDP), and deadline-based criteria. A model is developed to predict the behavior of ASViS across varying network conditions. Additionally, the effects of diverse parameters on ASViS performance are evaluated. ASViS adjusts data flow similarly to the Transmission Control Protocol (TCP), based on bandwidth availability. Data are designed to be discarded by ASViS according to video frame deadlines, preventing outdated information transmission. Compliance with RFC 8085 ensures the internet is not overwhelmed. With its scalability feature, ASViS achieves the highest possible image quality per frame, aligning with Scalable Video Coding (SVC) and the available data layers. The introduction of ASViS offers a promising approach to address the challenges faced by DASH, potentially providing more consistent and higher-quality video streaming. Full article
(This article belongs to the Section Computer Science & Engineering)
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17 pages, 3039 KiB  
Article
The Development of a Compact Pulsed Power Supply with Semiconductor Series Connection
by Shohei Zaizen, Kyohei Asami, Takashi Furukawa, Takeshi Hatta, Tsubasa Nakamura, Takashi Sakugawa and Takahisa Ueno
Electronics 2023, 12(21), 4541; https://doi.org/10.3390/electronics12214541 - 4 Nov 2023
Viewed by 1178
Abstract
In this study, high-voltage switching was performed by connecting semiconductors in series. By employing Snubber circuits and voltage divider resistors for each semiconductor, the destruction of the semiconductors was prevented. Additionally, a pulse transformer was installed between the function generator and the photocoupler [...] Read more.
In this study, high-voltage switching was performed by connecting semiconductors in series. By employing Snubber circuits and voltage divider resistors for each semiconductor, the destruction of the semiconductors was prevented. Additionally, a pulse transformer was installed between the function generator and the photocoupler to isolate the gate circuit, preventing electrical discharges in the circuit and enabling operation at an output voltage of 10 kV and an operating frequency of 200 Hz. The temperature of the semiconductors increased with the increase in operating frequency, which was counteracted by connecting charging resistors and capacitors to limit the current to the semiconductors. As a result, operation at 430 Hz became possible. Furthermore, a saturable inductor (SI) was connected to enable continuous operation. The SI delays the rise of the current and creates a phase difference, thereby reducing the power consumption of the conductor and mitigating the temperature rise, enabling continuous operation at 300 Hz. Moreover, by increasing the number of semiconductor series stages to six, an output voltage of 20 kV was confirmed in tests. By using two semiconductor series circuits, the pulsed power supply that can be changed to any pulse width was also created. As a result, output voltages with arbitrary pulse widths from 5 μs to 30 μs were confirmed. Full article
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26 pages, 2944 KiB  
Article
Implementation of Nonlinear Controller to Improve DC Microgrid Stability: A Comparative Analysis of Sliding Mode Control Variants
by Syeda Shafia Zehra, Alberto Dolara, Muhammad Ahsan Amjed and Marco Mussetta
Electronics 2023, 12(21), 4540; https://doi.org/10.3390/electronics12214540 - 4 Nov 2023
Cited by 3 | Viewed by 1144
Abstract
Electricity generation from sustainable renewable energy sources is constantly accelerating due to a rapid increase in demand from consumers. This requires an effective energy management and control system to fulfil the power demand without compromising the system’s performance. For this application, a nonlinear [...] Read more.
Electricity generation from sustainable renewable energy sources is constantly accelerating due to a rapid increase in demand from consumers. This requires an effective energy management and control system to fulfil the power demand without compromising the system’s performance. For this application, a nonlinear barrier sliding mode controller (BSMC) for a microgrid formed with PV, a fuel cell and an energy storage system comprising a battery and supercapacitor working in grid-connected mode is implemented. The advantages of the BSMC are twofold: The sliding surface oscillates in the close vicinity of zero by adapting an optimal gain value to ensure the smooth tracking of power to its references without overestimating the gains. Secondly, it exhibits a noticeable robustness to variations and disturbance, which is the bottleneck of the problem in a grid-connected mode. The stability of the presented controllers was analyzed with the Lyapunov stability criterion. Moreover, a comparison of the BSMC with sliding mode and supertwisting sliding mode controllers was carried out in MATLAB/Simulink (2020b) with real PV experimental data. The results and the numerical analysis verify the effectiveness of the BSMC in regulating the DC bus voltage in the presence of an external disturbance under varying conventional load and environmental conditions. Full article
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17 pages, 1127 KiB  
Article
Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
by Enzhi Zhou, Ziyue Liu, Ping Lan, Wei Xiao, Wei Yang and Xianhua Niu
Electronics 2023, 12(21), 4539; https://doi.org/10.3390/electronics12214539 - 4 Nov 2023
Viewed by 867
Abstract
This article investigates the transmission of downlink control signals for multiple unmanned aerial vehicle (UAV) clusters in collaborative search and rescue operations in mountainous environments. In this scenario, a reconfigurable intelligent surface (RIS) mounted on the UAV is utilized to overcome obstacles between [...] Read more.
This article investigates the transmission of downlink control signals for multiple unmanned aerial vehicle (UAV) clusters in collaborative search and rescue operations in mountainous environments. In this scenario, a reconfigurable intelligent surface (RIS) mounted on the UAV is utilized to overcome obstacles between the ground base station (BS) and UAVs. By leveraging the fixed channel of the RIS to the BS, the line-of-sight (LoS) path characteristics of the air-to-air channel, and the position information of the UAV, the RIS forms a directional beam by adjusting the RIS coefficient, which points towards UAVs in the cluster. To ensure low delay in control signaling and UAV state transmission, we adopt semi-persistent scheduling (SPS), which allocates pre-specified periodic intervals to each UAV for the formation of corresponding RIS coefficients. The allocation of time slots is constrained by the transmission intervals required by different UAVs and the number of RISs available. We propose a time slot scheduling scheme for UAVs to reduce inter-cluster interference caused by RIS beams. The time slot allocation problem is formulated as a combinatorial optimization problem. To solve this problem, we first propose an intuitive greedy scheme called local interference minimization (LIM). Building upon the LIM scheme, we propose a rollout-based algorithm called rollout interference minimization (RIM). Through simulation, we compare the LIM and RIM schemes with the benchmark scheduling scheme. The results demonstrate that our proposed scheme significantly reduces interference between UAV clusters while satisfying the conditions of periodic transmission and RIS quantity constraints. Full article
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19 pages, 9538 KiB  
Article
Utilising Artificial Intelligence to Turn Reviews into Business Enhancements through Sentiment Analysis
by Eliza Nichifor, Gabriel Brătucu, Ioana Bianca Chițu, Dana Adriana Lupșa-Tătaru, Eduard Mihai Chișinău, Raluca Dania Todor, Ruxandra-Gabriela Albu and Simona Bălășescu
Electronics 2023, 12(21), 4538; https://doi.org/10.3390/electronics12214538 - 4 Nov 2023
Viewed by 1908
Abstract
The use of sentiment analysis methodology has become crucial for e-commerce enterprises in order to optimise their marketing tactics. In the present setting, the authors strive to demonstrate the ethical and efficient use of artificial intelligence in the realm of business. The researchers [...] Read more.
The use of sentiment analysis methodology has become crucial for e-commerce enterprises in order to optimise their marketing tactics. In the present setting, the authors strive to demonstrate the ethical and efficient use of artificial intelligence in the realm of business. The researchers used qualitative research methodologies to analyse a total of 1687 evaluations obtained from 85 online retailers associated with electronic commerce Europe Trustmark. These stores were linked with 18 different nations and operated over 14 distinct domains. The investigation used the combined power of natural language processing and machine learning, implemented via a Software-as-a-Service (SaaS) platform. The results of the study indicate that consumers often exhibit a neutral emotional tone while leaving one-star ratings. Although the influence of unfavourable evaluations is generally limited, it highlights the need for more attentiveness in their management. The extent to which users interact with goods and services has a substantial impact on the probability of publishing reviews, regardless of whether the encountered experience is unpleasant or favourable. The authors urge for the acquisition of tools and skills in order to boost the efficiency of managers and experts in parallel with expanding technological landscapes, with a particular emphasis on the utilisation of artificial intelligence for sentiment analysis. Full article
(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)
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18 pages, 6684 KiB  
Article
Transmission Line Fault Detection and Classification Based on Improved YOLOv8s
by Hao Qiang, Zixin Tao, Bo Ye, Ruxue Yang and Weiyue Xu
Electronics 2023, 12(21), 4537; https://doi.org/10.3390/electronics12214537 - 4 Nov 2023
Viewed by 2467
Abstract
Transmission lines are an important component of the power grid, while complex natural conditions can cause fault and delayed maintenance, which makes it quite important to locate and collect the fault parts efficiently. The current unmanned aerial vehicle (UAV) inspection on transmission lines [...] Read more.
Transmission lines are an important component of the power grid, while complex natural conditions can cause fault and delayed maintenance, which makes it quite important to locate and collect the fault parts efficiently. The current unmanned aerial vehicle (UAV) inspection on transmission lines makes up for these problems to some extent. However, the complex background information contained in the images collected by power inspection and the existing deep learning methods are mostly highly sensitive to complex backgrounds, making the detection of multi-scale targets more difficult. Therefore, this article proposes an improved transmission line fault detection method based on YOLOv8s. The model not only detects defects in the insulators of power transmission lines but also adds the identification of birds’ nests, which makes the power inspection more comprehensive in detecting faults. This article uses Triplet Attention (TA) and an improved Bidirectional Feature Pyramid Network (BiFPN) to enhance the ability to extract discriminative features, enabling higher semantic information to be obtained after cross-layer fusion. Then, we introduce Wise-IoU (WIoU), a monotonic focus mechanism for cross-entropy, which enables the model to focus on difficult examples and improve the bounding box loss and classification loss. After deploying the improved method in the Win10 operating system and detecting insulator flashover, insulator broken, and nest faults, this article achieves a Precision of 92.1%, a Recall of 88.4%, and an mAP of 92.4%. Finally, we conclude that in complex background images, this method can not only detect insulator defects but also identify power tower birds’ nests. Full article
(This article belongs to the Special Issue Power System Fault Detection and Location Based on Machine Learning)
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21 pages, 2964 KiB  
Article
Graph-Indexed kNN Query Optimization on Road Network
by Wei Jiang, Guanyu Li, Mei Bai, Bo Ning, Xite Wang and Fangliang Wei
Electronics 2023, 12(21), 4536; https://doi.org/10.3390/electronics12214536 - 3 Nov 2023
Cited by 1 | Viewed by 613
Abstract
The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method’s inherent limitations [...] Read more.
The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method’s inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm. Full article
(This article belongs to the Special Issue Data Privacy in IoT Networks)
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24 pages, 6847 KiB  
Article
Image-Synthesis-Based Backdoor Attack Approach for Face Classification Task
by Hyunsik Na and Daeseon Choi
Electronics 2023, 12(21), 4535; https://doi.org/10.3390/electronics12214535 - 3 Nov 2023
Cited by 1 | Viewed by 916
Abstract
Although deep neural networks (DNNs) are applied in various fields owing to their remarkable performance, recent studies have indicated that DNN models are vulnerable to backdoor attacks. Backdoored images were generated by adding a backdoor trigger in original training images, which activated the [...] Read more.
Although deep neural networks (DNNs) are applied in various fields owing to their remarkable performance, recent studies have indicated that DNN models are vulnerable to backdoor attacks. Backdoored images were generated by adding a backdoor trigger in original training images, which activated the backdoor attack. However, most of the previously used attack methods are noticeable, not natural to the human eye, and easily detected by certain defense methods. Accordingly, we propose an image-synthesis-based backdoor attack, which is a novel approach to avoid this type of attack. To overcome the aforementioned limitations, we set a conditional facial region such as the hair, eyes, or mouth as a trigger and modified that region using an image synthesis technique that replaced the region of original image with the region of target image. Consequently, we achieved an attack success rate of up to 88.37% using 20% of the synthesized backdoored images injected in the training dataset while maintaining the model accuracy for clean images. Moreover, we analyzed the advantages of the proposed approach through image transformation, visualization of activation regions for DNN models, and human tests. In addition to its applicability in both label flipping and clean-label attack scenarios, the proposed method can be utilized as an attack approach to threaten security in the face classification task. Full article
(This article belongs to the Special Issue AI Security and Safety)
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29 pages, 20029 KiB  
Article
Offset Voltage Reduction in Two-Stage Folded-Cascode Operational Amplifier Using High-Precision Source Degeneration
by Cristian Stancu, Andrei Neacsu, Teodora Ionescu, Cornel Stanescu, Ovidiu Profirescu, Dragos Dobrescu and Lidia Dobrescu
Electronics 2023, 12(21), 4534; https://doi.org/10.3390/electronics12214534 - 3 Nov 2023
Cited by 2 | Viewed by 2320
Abstract
The demand for CMOS precision operational amplifiers for critical applications has continuously increased over time due to higher accuracy and sensitivity requirements. Trimming or chopper architectures are advanced solutions that reduce the offset voltage and improve the circuit’s parameters, but the complexity and [...] Read more.
The demand for CMOS precision operational amplifiers for critical applications has continuously increased over time due to higher accuracy and sensitivity requirements. Trimming or chopper architectures are advanced solutions that reduce the offset voltage and improve the circuit’s parameters, but the complexity and the increased chip die size are serious downsides. An efficient solution is a source degeneration configuration to control the transistor’s current-mirror transconductance, which impacts the offset voltage, with cost savings and a die area reduction also obtained. This paper focuses on designing and implementing such an approach in a two-stage folded-cascode operational amplifier. State-of-the-art thin-film resistors that use silicon–chromium as the metallic alloy were implemented to reduce mismatch variations between these passive components. Distinct methods that control the offset voltage parameter are also discussed and established. A comparison between the offset voltage standard deviation obtained using different types of resistors and that achieved with the innovative high-precision resistors was also carried out. The source degeneration’s impact on the common-mode rejection ratio, power supply rejection ratio, bandwidth and phase margin was also analyzed, and a comparison between the proposed design and the classical one was performed. The process variation’s influence on the circuit functionality was studied. A pre-layout ±1.273 mV maximum offset voltage at T = 27 °C was achieved using vector/array notations for the amplifier with the best overall performance. Post-layout simulations that included parasitic effects were performed, with a ±1.254 mV maximum offset voltage reached at room temperature. Full article
(This article belongs to the Special Issue CMOS Integrated Circuits Design)
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16 pages, 7581 KiB  
Article
Variable-Speed Frequency-Hopping Signal Sorting: Spectrogram Is Sufficient
by Weipeng Zhu, Hu Jin, Jin Wang, Yingke Lei, Caiyi Lou and Changming Liu
Electronics 2023, 12(21), 4533; https://doi.org/10.3390/electronics12214533 - 3 Nov 2023
Viewed by 597
Abstract
In this paper, we present a novel signal sorting method aimed at reducing the impact of interference and noise while achieving blind detection and accurate sorting of a variable-speed frequency-hopping communication system. To achieve this, we combine spectrogram analysis with an innovative sorting [...] Read more.
In this paper, we present a novel signal sorting method aimed at reducing the impact of interference and noise while achieving blind detection and accurate sorting of a variable-speed frequency-hopping communication system. To achieve this, we combine spectrogram analysis with an innovative sorting approach. First, we generate the spectrogram of the received signal, and then employ a morphology filter to effectively eliminate noise and sweep frequency interference from the spectrogram. Subsequently, we identify and mark connected domains in the spectrogram, from which we extract the duration data to create a dataset specifically for separating fixed-frequency interference. Furthermore, we propose a specialized time alignment algorithm designed to accommodate the unique characteristics of variable-speed frequency-hopping signals, enabling precise sorting of variable-speed frequency-hopping signals. Through rigorous comparative evaluations against existing algorithms, we demonstrate that our proposed approach provides superior accuracy by offering a clearer representation of the time–frequency situation of the received signals. The proposed method provides a high correct sorting probability which is equal to 0.8 when signal-to-noise ratio is 0 dB and reaches 1 when signal-to-noise ratio reaches over 12 dB. In comparison, the correct sorting probability of the comparison algorithm is far inferior to the proposed algorithm. Full article
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12 pages, 3801 KiB  
Article
Investigation of Contact Surface Changes and Sensor Response of a Pressure-Sensitive Conductive Elastomer
by Takeru Katagiri, Nguyen Chi Trung Ngo, Yuki Togawa, Sogo Kodama, Kotaro Kawahara, Kazuki Umemoto, Takanori Miyoshi and Tadachika Nakayama
Electronics 2023, 12(21), 4532; https://doi.org/10.3390/electronics12214532 - 3 Nov 2023
Cited by 1 | Viewed by 886
Abstract
The pressure-sensing mechanisms of conductive elastomers, such as conductive networks, and tunneling effects within them have been extensively studied. However, it has become apparent that external pressure can significantly impact the contact area of polymeric materials. In this study, we will employ a [...] Read more.
The pressure-sensing mechanisms of conductive elastomers, such as conductive networks, and tunneling effects within them have been extensively studied. However, it has become apparent that external pressure can significantly impact the contact area of polymeric materials. In this study, we will employ a commercially available conductive elastomer to investigate changes in resistance and contact surface under external pressure. Resistance measurements will be taken with and without applying conductive grease to the surface of the elastomer. This allows us to observe changes in resistance values associated with pressure variations. Furthermore, as pressure is applied to the conductive elastomer, the contact area ratio increases. Such an increase in the contact area and its correlation to changes in conductance values will be assessed. Full article
(This article belongs to the Section Electronic Materials)
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14 pages, 12541 KiB  
Article
BFE-Net: Object Detection with Bidirectional Feature Enhancement
by Rong Zhang, Zhongjie Zhu, Long Li, Yongqiang Bai and Jiong Shi
Electronics 2023, 12(21), 4531; https://doi.org/10.3390/electronics12214531 - 3 Nov 2023
Cited by 1 | Viewed by 770
Abstract
In realistic scenarios, existing object detection models still face challenges in resisting interference and detecting small objects due to complex environmental factors such as light and noise. For this reason, a novel scheme termed BFE-Net based on bidirectional feature enhancement is proposed. Firstly, [...] Read more.
In realistic scenarios, existing object detection models still face challenges in resisting interference and detecting small objects due to complex environmental factors such as light and noise. For this reason, a novel scheme termed BFE-Net based on bidirectional feature enhancement is proposed. Firstly, a new multi-scale feature extraction module is constructed, which uses a self-attention mechanism to simulate human visual perception. It is used to capture global information and long-range dependencies between pixels, thereby optimizing the extraction of multi-scale features from input images. Secondly, a feature enhancement and denoising module is designed, based on bidirectional information flow. In the top-down, the impact of noise on the feature map is weakened to further enhance the feature extraction. In the bottom-up, multi-scale features are fused to improve the accuracy of small object feature extraction. Lastly, a generalized intersection over union regression loss function is employed to optimize the movement direction of predicted bounding boxes, improving the efficiency and accuracy of object localization. Experimental results using the public dataset PASCAL VOC2007test show that our scheme achieves a mean average precision (mAP) of 85% for object detection, which is 2.3% to 8.6% higher than classical methods such as RetinaNet and YOLOv5. Particularly, the anti-interference capability and the performance in detecting small objects show a significant enhancement. Full article
(This article belongs to the Special Issue Deep Learning in Computer Vision and Image Processing)
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13 pages, 877 KiB  
Article
Modeling of Bacterial Cellulose-Based Composite
by Riccardo Caponetto, Giovanna Di Pasquale, Salvatore Graziani, Antonino Pollicino, Francesca Sapuppo and Carlo Trigona
Electronics 2023, 12(21), 4530; https://doi.org/10.3390/electronics12214530 - 3 Nov 2023
Viewed by 648
Abstract
Bio-derived polymers are promising materials for the development of eco-friendly sensors. Composites, composed of bacterial cellulose sheets sandwiched between two layers of conducting polymers and infused with ionic liquids, exhibit generating properties when utilized as deformation sensors. The composite material underwent a frequency [...] Read more.
Bio-derived polymers are promising materials for the development of eco-friendly sensors. Composites, composed of bacterial cellulose sheets sandwiched between two layers of conducting polymers and infused with ionic liquids, exhibit generating properties when utilized as deformation sensors. The composite material underwent a frequency analysis to explore the relationship between the transduction property and the frequency of the applied mechanical deformation. A model identification was performed using the acquired experimental data. The linearity of the system was examined, and the findings show that a second-order system, adequately approximates the system’s dynamics. Full article
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20 pages, 6751 KiB  
Article
Intelligent Frequency Decision Communication with Two-Agent Deep Reinforcement Learning
by Xin Liu, Mengqi Shi and Mei Wang
Electronics 2023, 12(21), 4529; https://doi.org/10.3390/electronics12214529 - 3 Nov 2023
Viewed by 734
Abstract
Traditional intelligent frequency-hopping anti-jamming technologies typically assume the presence of an ideal control channel. However, achieving this ideal condition in real-world confrontational environments, where the control channel can also be jammed, proves to be challenging. Regrettably, in the absence of a reliable control [...] Read more.
Traditional intelligent frequency-hopping anti-jamming technologies typically assume the presence of an ideal control channel. However, achieving this ideal condition in real-world confrontational environments, where the control channel can also be jammed, proves to be challenging. Regrettably, in the absence of a reliable control channel, the autonomous synchronization of frequency decisions becomes a formidable task, primarily due to the dynamic and heterogeneous nature of the transmitter and receiver’s spectral states. To address this issue, a novel communication framework for intelligent frequency decision is introduced, which operates without the need for negotiations. Furthermore, the frequency decision challenge between two communication terminals is formulated as a stochastic game, with each terminal’s utility designed to meet the requirements of a potential game. Subsequently, a two-agent deep reinforcement learning algorithm for best-response policy learning is devised to enable both terminals to achieve synchronization while avoiding jamming signals. Simulation results demonstrate that once the proposed algorithm converges, both communication terminals can effectively evade jamming signals. In comparison to existing similar algorithms, the throughput performance of this approach remains largely unaffected, with only a slightly extended convergence time. Notably, this performance is achieved without the need for negotiations, making the presented algorithm better suited for realistic scenarios. Full article
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20 pages, 3319 KiB  
Article
Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
by Yao Xiong and Shengming Jiang
Electronics 2023, 12(21), 4528; https://doi.org/10.3390/electronics12214528 - 3 Nov 2023
Viewed by 695
Abstract
Delay-tolerant networks face challenges in efficiently utilizing network resources and real-time sensing of node and message statuses due to the dynamic changes in their topology. In this paper, we propose a Multi-Decision Dynamic Intelligent (MDDI) routing protocol based on double Q-learning, node relationships, [...] Read more.
Delay-tolerant networks face challenges in efficiently utilizing network resources and real-time sensing of node and message statuses due to the dynamic changes in their topology. In this paper, we propose a Multi-Decision Dynamic Intelligent (MDDI) routing protocol based on double Q-learning, node relationships, and message attributes to achieve efficient message transmission. In the proposed protocol, the entire network is considered a reinforcement learning environment, with all mobile nodes treated as intelligent agents. Each node maintains two Q-tables, which store the Q-values corresponding to when a node forwards a message to a neighboring node. These Q-values are also related to the network’s average latency and average hop count. Additionally, we introduce node relationships to further optimize route selection. Nodes are categorized into three types of relationships: friends, colleagues, and strangers, based on historical interaction information, and message forwarding counts and remaining time are incorporated into the decision-making process. This protocol comprehensively takes into account the attributes of various resources in the network, enabling the dynamic adjustment of message-forwarding decisions as the network evolves. Simulation results show that the proposed multi-decision dynamic intelligent routing protocol achieves the highest message delivery rate as well as the lowest latency and overhead in all states of the network compared with other related routing protocols for DTNs. Full article
(This article belongs to the Section Networks)
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21 pages, 8627 KiB  
Article
An Efficient Inverse Synthetic Aperture Imaging Approach for Non-Cooperative Space Targets under Low-Signal-to-Noise-Ratio Conditions
by Zhijun Yang, Chengxiang Zhang, Dujuan Liang and Xin Xie
Electronics 2023, 12(21), 4527; https://doi.org/10.3390/electronics12214527 - 3 Nov 2023
Viewed by 621
Abstract
Due to the non-cooperative characteristics of space targets with complex motion, it is difficult to obtain high-quality inverse synthetic aperture (ISAR) images using conventional imaging approaches, posing a new challenge when designing novel approaches, especially under low-signal-to-noise-ratio (SNR) conditions. To overcome the obstacle [...] Read more.
Due to the non-cooperative characteristics of space targets with complex motion, it is difficult to obtain high-quality inverse synthetic aperture (ISAR) images using conventional imaging approaches, posing a new challenge when designing novel approaches, especially under low-signal-to-noise-ratio (SNR) conditions. To overcome the obstacle above, in this work, an efficient ISAR imaging approach based on high-order synchrosqueezing transform and modified multi-scale retinex (HSTMMSR) is proposed. First, the geometry and signal model of non-cooperative space targets with complex motion are established. Second, the echoes in each range bin are modeled as multi-component polynomial phase signals (MCPPSs) after correcting the translational migration and migration through range cells (MTRCs). Additionally, the time–frequency analysis (TFA) method based on HoSST is utilized to generate the time–frequency signal along with the azimuth dimension, where the coarse ISAR image is obtained with the quality indicator, e.g., image entropy, followed by the MMSR method to enhance the result. Both the simulated and measured data experiments validate the effectiveness and robustness of the proposed method. Full article
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32 pages, 5373 KiB  
Article
Optimization of Weight Matrices for the Linear Quadratic Regulator Problem Using Algebraic Closed-Form Solutions
by Daegyun Choi, Donghoon Kim and James D. Turner
Electronics 2023, 12(21), 4526; https://doi.org/10.3390/electronics12214526 - 3 Nov 2023
Viewed by 620
Abstract
This work proposes an analytical gradient-based optimization approach to determine the optimal weight matrices that make the state and control input at the final time close to zero for the linear quadratic regulator problem. Most existing methodologies focused on regulating the diagonal elements [...] Read more.
This work proposes an analytical gradient-based optimization approach to determine the optimal weight matrices that make the state and control input at the final time close to zero for the linear quadratic regulator problem. Most existing methodologies focused on regulating the diagonal elements using only bio-inspired approaches or analytical approaches. The method proposed, contrarily, optimizes both diagonal and off-diagonal matrix elements based on the gradient. Moreover, by introducing a new variable composed of the steady-state and time-varying terms for the Riccati matrix and using the coordinate transformation for the state, one develops algebraic equationsbased closed-form solutions to generate the required states and numerical partial derivatives for an optimization strategy that does not require the computationally intensive numerical integration process. The authors test the algorithm with one- and two-degrees-of-freedom linear plant models, and it yields the weight matrices that successfully satisfy the pre-defined requirement, which is the norm of the augmented states less than 10−5. The results suggest the broad applicability of the proposed algorithm in science and engineering. Full article
(This article belongs to the Collection Predictive and Learning Control in Engineering Applications)
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17 pages, 5061 KiB  
Article
Research on the Current Control Strategy of a Brushless DC Motor Utilizing Infinite Mixed Sensitivity Norm
by Tianqing Yuan, Jiu Chang and Yupeng Zhang
Electronics 2023, 12(21), 4525; https://doi.org/10.3390/electronics12214525 - 3 Nov 2023
Viewed by 1191
Abstract
During the brushless DC (BLDC) motor working process, the system encounters inevitable uncertainties. These ambiguities stem from potential fluctuations, random occurrences, measurement inaccuracies, varying operational conditions, environmental shifts such as temperature alterations, among other factors. Uncertainties, an inherent aspect of any real control [...] Read more.
During the brushless DC (BLDC) motor working process, the system encounters inevitable uncertainties. These ambiguities stem from potential fluctuations, random occurrences, measurement inaccuracies, varying operational conditions, environmental shifts such as temperature alterations, among other factors. Uncertainties, an inherent aspect of any real control system, can be broadly classified into two categories: sensor signal uncertainties and discrepancies between the mathematical and actual models due to parameter perturbations. To mitigate the impact of sensor noise and parameter perturbations on the BLDC motor, a robust control strategy utilizing infinite norm mixed sensitivity based on PI control strategy (PI-H∞-MIX) is proposed in this paper. Firstly, the closed-loop control structure and transfer function model of the BLDC motor control system current loop are analyzed based on the current loop circuit topology, and then, the model parameters perturbation is analyzed, and the multiplicative uncertainty bound is given. In addition, the appropriate weighting function is selected to ensure the robustness of the system. In this case, the controller design problem is transformed into the H∞ standard control problem, and then, the system augmentation matrix is established, and the controller is solved by Matlab/Simulink. Finally, the performances of the traditional PI control strategy and the PI-H∞-MIX are compared and analyzed. The results show that (1) the proposed PI-H∞-MIX strategy can improve the control system robustness under the parameter perturbation condition effectively, and (2) the proposed PI-H∞-MIX strategy can suppress the noise signal of the sensor. Full article
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26 pages, 1583 KiB  
Review
Pedagogical Design Considerations for Mobile Augmented Reality Serious Games (MARSGs): A Literature Review
by Cassidy R. Nelson and Joseph L. Gabbard
Electronics 2023, 12(21), 4524; https://doi.org/10.3390/electronics12214524 - 3 Nov 2023
Viewed by 951
Abstract
As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine [...] Read more.
As technology advances, conceptualizations of effective strategies for teaching and learning shift. Due in part to their facilitation of unique affordances for learning, mobile devices, augmented reality, and games are all becoming more prominent elements in learning environments. In this work, we examine mobile augmented reality serious games (MARSGs) as the intersection of these technology-based experiences and to what effect their combination can yield even greater learning outcomes. We present a PRISMA review of 23 papers (from 610) spanning the entire literature timeline from 2002–2023. Among these works, there is wide variability in the realized application of game elements and pedagogical theories underpinning the game experience. For an educational tool to be effective, it must be designed to facilitate learning while anchored by pedagogical theory. Given that most MARSG developers are not pedagogical experts, this review further provides design considerations regarding which game elements might proffer the best of three major pedagogical theories for modern learning (cognitive constructivism, social constructivism, and behaviorism) based on existing applications. We will also briefly touch on radical constructivism and the instructional elements embedded within MARSGs. Lastly, this work offers a synthesis of current MARSG findings and extended future directions for MARSG development. Full article
(This article belongs to the Special Issue Serious Games and Extended Reality (XR))
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