Next Issue
Volume 12, May-2
Previous Issue
Volume 12, April-2
 
 

Electronics, Volume 12, Issue 9 (May-1 2023) – 196 articles

Cover Story (view full-size image): Tracking control of nonlinear dynamical systems with system uncertainties, unknown disturbances, and noise is a challenging task. Therefore, to compensate for system uncertainties and unknown disturbances, this paper presents a trajectory tracking control strategy for a class of nonlinear dynamical systems by employing a gradient descent-based simple learning control strategy that minimizes the cost function corresponding to the desired closed-loop error dynamics of the system. A stability proof for the closed-loop nonlinear system is provided based on the pseudo-linear system theory. The learning capability of the designed controller makes it suitable to take system uncertainties and unknown disturbances into account as demonstrated through simulations of a PPR robot. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
9 pages, 712 KiB  
Communication
Multiple Signal TDOA/FDOA Joint Estimation with Coherent Integration
by Xinxin Ouyang, Shanfeng Yao and Qun Wan
Electronics 2023, 12(9), 2151; https://doi.org/10.3390/electronics12092151 - 08 May 2023
Viewed by 1077
Abstract
Passive localization relies significantly on the estimation of the Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) to accurately determine the location of a target. The precision of TDOA and FDOA estimation is affected by signal parameters of time and [...] Read more.
Passive localization relies significantly on the estimation of the Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) to accurately determine the location of a target. The precision of TDOA and FDOA estimation is affected by signal parameters of time and frequency distribution. In case of multiple signals arising at different frequency bands and intercepted simultaneously by spatially separate sensors covering a wide frequency band, the traditional method is first to separate the signals from the mixed wideband signal through digital down conversion (DDC), which brings multiple narrowband signals, and then the estimation of TDOA and FDOA of each narrowband signal can be performed using cross ambiguity function (CAF). The paper introduces a novel approach for estimating TDOA and FDOA of multiple signals simultaneously, which employs a coherent integration method. First, the cross ambiguity function for each signal is realized with the narrowband signal as the same as the traditional method. Next, the phase relation of each CAF is analyzed, then the joint CAF can be obtained with phase compensation, from which multiple signal TDOA and FDOA estimations will be implemented simultaneously. Numerical simulations are performed to compare the two methods, and the results demonstrate the superiority of the proposed algorithm. Full article
Show Figures

Figure 1

18 pages, 4067 KiB  
Article
An Efficient Adaptive Noise Removal Filter on Range Images for LiDAR Point Clouds
by Minh-Hai Le, Ching-Hwa Cheng and Don-Gey Liu
Electronics 2023, 12(9), 2150; https://doi.org/10.3390/electronics12092150 - 08 May 2023
Cited by 5 | Viewed by 2376
Abstract
Light Detection and Ranging (LiDAR) is a critical sensor for autonomous vehicle systems, providing high-resolution distance measurements in real-time. However, adverse weather conditions such as snow, rain, fog, and sun glare can affect LiDAR performance, requiring data preprocessing. This paper proposes a novel [...] Read more.
Light Detection and Ranging (LiDAR) is a critical sensor for autonomous vehicle systems, providing high-resolution distance measurements in real-time. However, adverse weather conditions such as snow, rain, fog, and sun glare can affect LiDAR performance, requiring data preprocessing. This paper proposes a novel approach, the Adaptive Outlier Removal filter on range Image (AORI), which combines a projection image from LiDAR point clouds with an adaptive outlier removal filter to remove snow particles. Our research aims to analyze the characteristics of LiDAR and propose an image-based approach derived from LiDAR data that addresses the limitations of previous studies, particularly in improving the efficiency of nearest neighbor point search. Our proposed method achieves outstanding performance in both accuracy (>96%) and processing speed (0.26 s per frame) for autonomous driving systems under harsh weather from raw LiDAR point clouds in the Winter Adverse Driving dataset (WADS). Notably, AORI outperforms state-of-the-art filters by achieving a 6.6% higher F1 score and 0.7% higher accuracy. Although our method has a lower recall than state-of-the-art methods, it achieves a good balance between retaining object points and filter noise points from LiDAR, indicating its promise for snow removal in adverse weather conditions. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Based Autonomous Systems)
Show Figures

Figure 1

12 pages, 7785 KiB  
Article
A Compact 2.4 GHz L-Shaped Microstrip Patch Antenna for ISM-Band Internet of Things (IoT) Applications
by Muhammad Fitra Zambak, Samir Salem Al-Bawri, Muzammil Jusoh, Ali Hanafiah Rambe, Hamsakutty Vettikalladi, Ali M. Albishi and Mohamed Himdi
Electronics 2023, 12(9), 2149; https://doi.org/10.3390/electronics12092149 - 08 May 2023
Cited by 2 | Viewed by 2911
Abstract
Wireless communication technology integration is necessary for Internet of Things (IoT)-based applications to make their data easily accessible. This study proposes a new, portable L-shaped microstrip patch antenna with enhanced gain for IoT 2.4 GHz Industrial, Scientific, and Medical (ISM) applications. The overall [...] Read more.
Wireless communication technology integration is necessary for Internet of Things (IoT)-based applications to make their data easily accessible. This study proposes a new, portable L-shaped microstrip patch antenna with enhanced gain for IoT 2.4 GHz Industrial, Scientific, and Medical (ISM) applications. The overall dimensions of the antenna are 28 mm × 21 mm × 1.6 mm (0.22λo × 0.17λo × 0.013λo, with respect to the lowest frequency). The antenna design is simply comprised of an L-shape strip line, with a full ground applied in the back side and integrated with a tiny rectangular slot. According to investigations, the developed antenna is more efficient and has a greater gain than conventional antennas. The flexibility of the antenna’s matching impedance and performance are investigated through several parametric simulations. Results indicate that the gain and efficiency can be enhanced through modifying the rectangular back slot in conjunction with fine-tuning the front L-shaped patch. The finalized antenna operates at 2.4 GHz with a 98% radiation efficiency and peak gains of 2.09 dBi (measured) and 1.95 dBi (simulated). The performance of the simulation and measurement are found to be in good agreement. Based on the performance that was achieved, the developed L-shaped antenna can be used in a variety of 2.4 GHz ISM bands and IoT application environments, especially for indoor localization estimation scenarios, such as smart offices and houses, and fourth-generation (4G) wireless communications applications due to its small size and high fractional bandwidth. Full article
(This article belongs to the Special Issue Antennas for IoT Devices)
Show Figures

Figure 1

13 pages, 3174 KiB  
Article
Detection of Secondary Side Position for Segmented Dynamic Wireless Charging Systems Based on Primary Phase Angle Sensing
by Wei Xiong, Jiangtao Liu, Jing Chen and Dewang Hu
Electronics 2023, 12(9), 2148; https://doi.org/10.3390/electronics12092148 - 08 May 2023
Viewed by 918
Abstract
In dynamic wireless charging systems, the detection of secondary side positions has been attracting much attention in academic research. Due to the strong electromagnetic interference and the presence of foreign objects in the charging area, the use of conventional detection methods such as [...] Read more.
In dynamic wireless charging systems, the detection of secondary side positions has been attracting much attention in academic research. Due to the strong electromagnetic interference and the presence of foreign objects in the charging area, the use of conventional detection methods such as wireless communication and infrared techniques may be problematic; therefore, as an alternative to solve the above problem, a new detection method based on phase angle sensing is proposed in this paper. Through phase analysis of the primary input impedance and by reference to the relationship between the input port phase angle and the secondary side position, the proposed method is able to sense the secondary side position in real time. In addition, an analysis of the sensitivity of the proposed method to parameter variations is also carried out. In order to verify the effectiveness of the proposed position detection method, a dynamic wireless charging system with four segments is built for experimental verification. The experimental results show that when the phase angle threshold is set at 300°, the secondary side position can be accurately identified, and the proposed method is quite robust within a parameter deviation of up to 4%. Full article
Show Figures

Figure 1

10 pages, 697 KiB  
Communication
High-Precision Fitting of Simulation Parameters for Circuit Aging Effect
by Xinhuan Yang, Qianqian Sang, Jianyu Zhang, Shuo Wang, Chuanzheng Wang, Mingyan Yu and Yuanfu Zhao
Electronics 2023, 12(9), 2147; https://doi.org/10.3390/electronics12092147 - 08 May 2023
Viewed by 1078
Abstract
To take the influence of the aging effect on circuit performance into account at the early design stage, it is necessary to establish an accurate aging simulation model. However, there is a great discrepancy in the reversely deduced MOSFET transistor degradation from the [...] Read more.
To take the influence of the aging effect on circuit performance into account at the early design stage, it is necessary to establish an accurate aging simulation model. However, there is a great discrepancy in the reversely deduced MOSFET transistor degradation from the aging model. To deal with that problem, a method is proposed in this paper that establishes the conversion relationship between the simulation parameters and the degradation of MOSFET transistor parameters. The degradation values were converted into model parameters that characterize the aging effect in SPICE, and the results show that aging simulation accuracy was improved to within 0.1%, which would bring great convenience to circuit reliability simulation and analysis. Lastly, we analyze the aging effect on the ring oscillator circuit via the model. Full article
Show Figures

Figure 1

19 pages, 4845 KiB  
Article
Using CNN with Multi-Level Information Fusion for Image Denoising
by Shaodong Xie, Jiagang Song, Yuxuan Hu, Chengyuan Zhang and Shichao Zhang
Electronics 2023, 12(9), 2146; https://doi.org/10.3390/electronics12092146 - 08 May 2023
Cited by 1 | Viewed by 1910
Abstract
Deep convolutional neural networks (CNN) with hierarchical architectures have obtained good results for image denoising. However, in some cases where the noise level is unknown and the image background is complex, it is challenging to obtain robust information through CNN. In this paper, [...] Read more.
Deep convolutional neural networks (CNN) with hierarchical architectures have obtained good results for image denoising. However, in some cases where the noise level is unknown and the image background is complex, it is challenging to obtain robust information through CNN. In this paper, we present a multi-level information fusion CNN (MLIFCNN) in image denoising containing a fine information extraction block (FIEB), a multi-level information interaction block (MIIB), a coarse information refinement block (CIRB), and a reconstruction block (RB). In order to adapt to more complex image backgrounds, FIEB uses parallel group convolution to extract wide-channel information. To enhance the robustness of the obtained information, a MIIB uses residual operations to act in two sub-networks for implementing the interaction of wide and deep information to adapt to the distribution of different noise levels. To enhance the stability of the training denoiser, CIRB stacks common and group convolutions to refine the obtained information. Finally, RB uses a residual operation to act in a single convolution in order to obtain the resultant clean image. Experimental results show that our method is better than many other excellent methods, both in terms of quantitative and qualitative aspects. Full article
(This article belongs to the Special Issue Big Model Techniques for Image Processing)
Show Figures

Figure 1

10 pages, 5416 KiB  
Communication
A 3.7 GHz CPW Filtering Antenna with a Pair of Gain Zeros
by Yulei Xue, Yanchen Dong, Weiping Huang, Ruiqiang Yan, Jiaqiang Xiang and Peng Wang
Electronics 2023, 12(9), 2145; https://doi.org/10.3390/electronics12092145 - 08 May 2023
Cited by 2 | Viewed by 1221
Abstract
In recent years, there has been a development of multifunctional components, including filtering antennas. Co-designing the filter and antenna can effectively reduce the overall size and thus realize high integration. A new coplanar waveguide (CPW) omnidirectional filtering antenna with a pair of gain [...] Read more.
In recent years, there has been a development of multifunctional components, including filtering antennas. Co-designing the filter and antenna can effectively reduce the overall size and thus realize high integration. A new coplanar waveguide (CPW) omnidirectional filtering antenna with a pair of gain zeros is presented using separation electric/magnetic coupling (SEMC) paths that greatly enhance frequency selectivity and effectively suppress the spurious responses of the circular patch radiator. The impedance bandwidth of the filtering antenna can also be controlled/adjusted through electric/magnetic coupling. The design results demonstrate that the filtering antenna achieves an omnidirectional pattern with a center frequency of 3.7 GHz, an impedance bandwidth of about 3%, a gain of 2.7 dBi, and a pair of gain zeros. Full article
Show Figures

Figure 1

14 pages, 10031 KiB  
Article
TiN-NbN-TiN and Permalloy Nanostructures for Applications in Transmission Electron Microscopy
by Michael I. Faley, Joshua Williams, Penghan Lu and Rafal E. Dunin-Borkowski
Electronics 2023, 12(9), 2144; https://doi.org/10.3390/electronics12092144 - 08 May 2023
Cited by 1 | Viewed by 1501
Abstract
We fabricated superconducting and ferromagnetic nanostructures, which are intended for applications in transmission electron microscopy (TEM), in a commercial sample holder that can be cooled using liquid helium. Nanoscale superconducting quantum-interference devices (nanoSQUIDs) with sub-100 nm nanobridge Josephson junctions (nJJs) were prepared at [...] Read more.
We fabricated superconducting and ferromagnetic nanostructures, which are intended for applications in transmission electron microscopy (TEM), in a commercial sample holder that can be cooled using liquid helium. Nanoscale superconducting quantum-interference devices (nanoSQUIDs) with sub-100 nm nanobridge Josephson junctions (nJJs) were prepared at a distance of ~300 nm from the edges of a 2 mm × 2 mm × 0.05 mm substrate. Thin-film TiN-NbN-TiN heterostructures were used to optimize the superconducting parameters and enhance the oxidation and corrosion resistance of nJJs and nanoSQUIDs. Non-hysteretic I(V) characteristics of nJJs, as well as peak-to-peak quantum oscillations in the V(B) characteristics of the nanoSQUIDs with an amplitude of up to ~20 µV, were obtained at a temperature ~5 K, which is suitable for operation in TEM. Electron-beam lithography, high-selectivity reactive ion etching with pure SF6 gas, and a naturally created undercut in the Si substrate were used to prepare nanoSQUIDs on a SiN membrane within ~500 nm from the edge of the substrate. Permalloy nanodots with diameters down to ~100 nm were prepared on SiN membranes using three nanofabrication methods. High-resolution TEM revealed that permalloy films on a SiN buffer have a polycrystalline structure with an average grain dimension of approximately 5 nm and a lattice constant of ~0.36 nm. The M(H) dependences of the permalloy films were measured and revealed coercive fields of 2 and 10 G at 300 and 5 K, respectively. These technologies are promising for the fabrication of superconducting electronics based on nJJs and ferromagnetic nanostructures for operation in TEM. Full article
(This article belongs to the Special Issue Nanofabrication of Superconducting Circuits)
Show Figures

Figure 1

17 pages, 1615 KiB  
Article
Divergent Selection Task Offloading Strategy for Connected Vehicles Based on Incentive Mechanism
by Senyu Yu, Yan Guo, Ning Li, Duan Xue and Hao Yuan
Electronics 2023, 12(9), 2143; https://doi.org/10.3390/electronics12092143 - 08 May 2023
Viewed by 1194
Abstract
With the improvements in the intelligent level of connected vehicles (CVs), travelers can enjoy services such as self-driving, self-parking and audiovisual entertainment inside the vehicle, which place extremely high demands on the computing power of onboard systems (OBSs). However, the arithmetic power of [...] Read more.
With the improvements in the intelligent level of connected vehicles (CVs), travelers can enjoy services such as self-driving, self-parking and audiovisual entertainment inside the vehicle, which place extremely high demands on the computing power of onboard systems (OBSs). However, the arithmetic power of a single CV often cannot meet the diverse service demands of the in-vehicle system. As a new computing paradigm, task offloading based on vehicular edge computing has significant advantages in remedying the shortcomings of single-CV computing power and balancing the allocation of computing resources. This paper studied the computational task offloading of high-speed connected vehicles without the help of roadside edge servers in certain geographic areas. User vehicles (UVs) with insufficient computing power offload some of their computational tasks to nearby CVs with abundant resources. We explored the high-speed driving model and task classification model of CVs to refine the task offloading process. Additionally, inspired by game theory, we designed a divergent selection task offloading strategy based on an incentive mechanism (DSIM), in which we balanced the interests of both the user vehicle and service vehicles. CVs that contribute resources are rewarded to motivate more CVs to join. A DSIM algorithm based on a divergent greedy algorithm was introduced to maximize the total rewards of all volunteer vehicles while respecting the will of both the user vehicle and service vehicles. The experimental simulation results showed that, compared with several existing studies, our approach can always obtain the highest reward for service vehicles and lowest latency for user vehicles. Full article
Show Figures

Figure 1

14 pages, 2496 KiB  
Article
On-Demand Garbage Collection Algorithm with Prioritized Victim Blocks for SSDs
by Hyeyun Lee, Wooseok Choi and Youpyo Hong
Electronics 2023, 12(9), 2142; https://doi.org/10.3390/electronics12092142 - 07 May 2023
Viewed by 1624
Abstract
Because of their numerous benefits, solid-state drives (SSDs) are increasingly being used in a wide range of applications, including data centers, cloud computing, and high-performance computing. The growing demand for SSDs has led to a continuous improvement in their technology and a reduction [...] Read more.
Because of their numerous benefits, solid-state drives (SSDs) are increasingly being used in a wide range of applications, including data centers, cloud computing, and high-performance computing. The growing demand for SSDs has led to a continuous improvement in their technology and a reduction in their cost, making them a more accessible storage solution for a wide range of users. Garbage collection (GC) is a process that reclaims wasted storage space in NAND flash memories, which are used as the memory devices for SSDs. However, the GC process can cause performance degradation and lifetime reduction. This paper proposes an efficient garbage collection (GC) scheme that minimizes overhead by invoking GC operations only when necessary. Each GC operation is executed in a specific order based on the expected storage gain and the execution cost, ensuring that the storage space requirement is met while minimizing the frequency of GC invocation. This approach not only reduces the overhead due to GC, but also improves the overall performance of SSDs, including the latency and write amplification factor (WAF) which is an important indicator of the longevity of SSDs. Full article
Show Figures

Figure 1

10 pages, 407 KiB  
Article
Deep Learning-Based Time Delay Estimation Using Ground Penetrating Radar
by Feng Lin, Meng Sun, Shiyu Mao and Bin Wang
Electronics 2023, 12(9), 2141; https://doi.org/10.3390/electronics12092141 - 07 May 2023
Viewed by 1250
Abstract
Time delay estimation (TDE) is of great interest for the thickness estimation of pavement using ground penetrating radar (a non-destructive testing tool that uses electromagnetic waves to probe civil engineering material), which determines the difference between the times of arrival of two incoming [...] Read more.
Time delay estimation (TDE) is of great interest for the thickness estimation of pavement using ground penetrating radar (a non-destructive testing tool that uses electromagnetic waves to probe civil engineering material), which determines the difference between the times of arrival of two incoming signals or backscattered echoes. However, conventional TDE methods suffer performance degradation because of limited resolution for thin layers and highly correlated backscattered echoes. In this paper, a deep neural network (DNN)-based TDE method is proposed. Firstly, a new DNN is constructed to classify and train the backscattered echoes; then, the time delays of the backscattered echoes can be estimated through the proposed DNN. The proposed method is based on the data processing of the backscattered echoes, which is more robust to the noise than conventional subspace-based methods (MUSIC, ESPRIT) and compressive sensing-based methods (OMP). The proposed method can directly process coherent backscattered echoes without decorrelation procedures, compared with MUSIC and ESPRIT. In addition, the proposed method is more powerful in resolving the close backscattered echoes than that of OMP. Simulation results show the efficiency of the proposed method in terms of signal-to-noise ratio (SNR) and BΔτ products. (The BΔτ products indicate the resolution of GPR, B is the frequency bandwidth of GPR and Δτ is the time delay between two incoming signals or backscattered echoes). Full article
(This article belongs to the Special Issue Sparse Array Design, Processing and Application)
Show Figures

Figure 1

19 pages, 2821 KiB  
Article
Blockchain-Based Authentication Protocol Design from a Cloud Computing Perspective
by Zhiqiang Du, Wenlong Jiang, Chenguang Tian, Xiaofeng Rong and Yuchao She
Electronics 2023, 12(9), 2140; https://doi.org/10.3390/electronics12092140 - 07 May 2023
Cited by 4 | Viewed by 1383
Abstract
Cloud computing is a disruptive technology that has transformed the way people access and utilize computing resources. Due to the diversity of services and complexity of environments, there is widespread interest in how to securely and efficiently authenticate users under the same domain. [...] Read more.
Cloud computing is a disruptive technology that has transformed the way people access and utilize computing resources. Due to the diversity of services and complexity of environments, there is widespread interest in how to securely and efficiently authenticate users under the same domain. However, many traditional authentication methods involve untrusted third parties or overly centralized central authorities, which can compromise the security of the system. Therefore, it is crucial to establish secure authentication channels within trusted domains. In this context, we propose a secure and efficient authentication protocol, HIDA (Hyperledger Fabric Identity Authentication), for the cloud computing environment. Specifically, by introducing federated chain technology to securely isolate entities in the trust domain, and combining it with zero-knowledge proof technology, users’ data are further secured. In addition, Subsequent Access Management allows users to prove their identity by revealing only brief credentials, greatly improving the efficiency of access. To ensure the security of the protocol, we performed a formal semantic analysis and proved that it can effectively protect against various attacks. At the same time, we conducted ten simulations to prove that the protocol is efficient and reliable in practical applications. The research results in this paper can provide new ideas and technical support for identity authentication in a cloud environment and provide a useful reference for realizing the authentication problem in cloud computing application scenarios. Full article
(This article belongs to the Special Issue Advancement in Blockchain Technology and Applications)
Show Figures

Figure 1

20 pages, 9720 KiB  
Article
Limits on Cooperative Positioning for a Robotic Swarm with Time of Flight Ranging over Two-Ray Ground Reflection Channel
by Emanuel Staudinger, Robert Pöhlmann, Armin Dammann and Siwei Zhang
Electronics 2023, 12(9), 2139; https://doi.org/10.3390/electronics12092139 - 07 May 2023
Viewed by 967
Abstract
Autonomous robotic swarms are envisioned for a variety of applications—for example, space exploration, search and rescue, and disaster management. Important features of a robotic swarm include its ability to share information within the network, to sense spatio-temporal processes such as gas distributions, and [...] Read more.
Autonomous robotic swarms are envisioned for a variety of applications—for example, space exploration, search and rescue, and disaster management. Important features of a robotic swarm include its ability to share information within the network, to sense spatio-temporal processes such as gas distributions, and to collaboratively enhance its navigation. In environments without infrastructure, the swarm elements can cooperatively estimate their position, e.g., based on the time of flight of exchanged radio signals. Cooperative positioning performance depends on the radio propagation environment. Free-space path loss is commonly used for performance assessment, which is an optimistic assumption. In this work, we investigate the limits to cooperative positioning and ranging based on the time of flight of radio signals over the more realistic two-ray ground reflection channel. We show that we obtain a ranging bias caused by the radio signal component reflected from the ground, and that the ranging error becomes bias-limited. In the positioning domain, we investigate how the ranging bias affects the cooperative positioning performance. As a result, we gain in cooperation, but the achievable positioning performance is significantly worsened by the ranging bias. As a conclusion, the two-ray ground reflection model should be considered to obtain realistic cooperative positioning limits. Full article
(This article belongs to the Special Issue Swarm Communication, Localization and Navigation)
Show Figures

Figure 1

18 pages, 6990 KiB  
Article
Cable Broken Wire Signal Recognition Based on Convolutional Neural Network
by Wanxu Zhu, Runzi Liu, Peng Jiang and Jiazhu Huang
Electronics 2023, 12(9), 2138; https://doi.org/10.3390/electronics12092138 - 07 May 2023
Viewed by 1542
Abstract
Due to the long-term exposure of bridge ties to complex environments, their internal steel wires are prone to corrosion damage, which may lead to tie breakage accidents if not detected in time. Although existing advanced monitoring methods can be used to obtain the [...] Read more.
Due to the long-term exposure of bridge ties to complex environments, their internal steel wires are prone to corrosion damage, which may lead to tie breakage accidents if not detected in time. Although existing advanced monitoring methods can be used to obtain the broken wire signal, they either still need the damage to be identified manually or are limited by the training data set. To address this problem, a model combination consisting of a classification model and three regression models was built based on convolutional neural networks to predict the location of broken wires after first classifying them based on features. We developed software-containing data set generation and model performance testing functions, in which we used original algorithms to expand the broken wire data set for training based on the measured data obtained from FBG sensors with a sampling frequency of 100 Hz, thus generating more than 22,000 types of data. The performance test results showed that the model combination successfully detected 11,972 broken wires among 12,000 test data points generated by the algorithm, with a recognition success rate of 99.77% and an average time of 0.0076 s between the predicted location and the actual broken wire location, with an error rate of 0.38%. In the test of 118 real broken wires, the model detected all the abnormalities, and the average time between the predicted location and the actual broken wire location was 0.0695 s, with an error of 3.48%. This verified the feasibility of using artificial intelligence to accurately identify broken wire signals and can provide a reference for the subsequent intelligent identification of tie abnormalities. Full article
(This article belongs to the Special Issue Machine Learning for Signals Processing)
Show Figures

Figure 1

24 pages, 669 KiB  
Article
Complement Recognition-Based Formal Concept Analysis for Automatic Extraction of Interpretable Concept Taxonomies from Text
by Stefano Ferilli
Electronics 2023, 12(9), 2137; https://doi.org/10.3390/electronics12092137 - 07 May 2023
Viewed by 1047
Abstract
The increasing scale and pace of the production of digital documents have generated a need for automatic tools to analyze documents and extract underlying concepts and knowledge in order to help humans manage information overload. Specifically, since most information comes in the form [...] Read more.
The increasing scale and pace of the production of digital documents have generated a need for automatic tools to analyze documents and extract underlying concepts and knowledge in order to help humans manage information overload. Specifically, since most information comes in the form of text, natural language processing tools are needed that are able to analyze the sentences and transform them into an internal representation that can be handled by computers to perform inferences and reasoning. In turn, these tools often work based on linguistic resources for the various levels of analysis (morphological, lexical, syntactic and semantic). The resources are language (and sometimes even domain) specific and typically must be manually produced by human experts, increasing their cost and limiting their availability. Especially relevant are concept taxonomies, which allow us to properly interpret the textual content of documents. This paper presents an intelligent module to extract relevant domain knowledge from free text by means of Concept Hierarchy Extraction techniques. In particular, the underlying model is provided using Formal Concept Analysis, while a crucial role is played by an expert system for language analysis that can recognize different types of indirect objects (a component very rich in information) in English. Full article
(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)
Show Figures

Figure 1

24 pages, 717 KiB  
Article
DiffuD2T: Empowering Data-to-Text Generation with Diffusion
by Heng Gong, Xiaocheng Feng and Bing Qin
Electronics 2023, 12(9), 2136; https://doi.org/10.3390/electronics12092136 - 07 May 2023
Viewed by 2184
Abstract
Surrounded by structured data, such as medical data, financial data, knowledge bases, etc., data-to-text generation has become an important natural language processing task that can help people better understand the meaning of those data by providing them with user-friendly text. Existing methods for [...] Read more.
Surrounded by structured data, such as medical data, financial data, knowledge bases, etc., data-to-text generation has become an important natural language processing task that can help people better understand the meaning of those data by providing them with user-friendly text. Existing methods for data-to-text generation show promising results in tackling two major challenges: content planning and surface realization, which transform structured data into fluent text. However, they lack an iterative refinement process for generating text, which can enable the model to perfect the text step-by-step while accepting control over the process. In this paper, we explore enhancing data-to-text generation with an iterative refinement process via diffusion. We have four main contributions: (1) we use the diffusion model to improve the prefix tuning for data-to-text generation; (2) we propose a look-ahead guiding loss to supervise the iterative refinement process for better text generation; (3) we extract content plans from reference text and propose a planning-then-writing pipeline to give the model content planning ability; and (4) we conducted experiments on three data-to-text generation datasets and both automatic evaluation criteria (BLEU, NIST, METEOR, ROUGEL, CIDEr, TER, MoverScore, BLEURT, and BERTScore) and human evaluation criteria (Quality and Naturalness) show the effectiveness of our model. Our model can improve the competitive prefix tuning method by 2.19% in terms of a widely-used automatic evaluation criterion BLEU (BiLingual Evaluation Understudy) on WebNLG dataset with GPT-2 Large as the pretrained language model backbone. Human evaluation criteria also show that our model can improve the quality and naturalness of the generated text across all three datasets. Full article
(This article belongs to the Special Issue Natural Language Processing and Information Retrieval)
Show Figures

Figure 1

17 pages, 4639 KiB  
Article
Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-II
by Nibedita Rout, Jinlian Hu, George Baciu, Priyabrata Pattanaik, K. Nakkeeran and Asimananda Khandual
Electronics 2023, 12(9), 2135; https://doi.org/10.3390/electronics12092135 - 06 May 2023
Viewed by 1579
Abstract
The extended application of device-dependent systems’ vision is growing exponentially, but these systems face challenges in precisely imitating the human perception models established by the device-independent systems of the Commission internationale de l’éclairage (CIE). We previously discussed the theoretical treatment and experimental validation [...] Read more.
The extended application of device-dependent systems’ vision is growing exponentially, but these systems face challenges in precisely imitating the human perception models established by the device-independent systems of the Commission internationale de l’éclairage (CIE). We previously discussed the theoretical treatment and experimental validation of developing a calibrated integrated sphere imaging system to imitate the visible spectroscopy environment. The RGB polynomial function was derived to obtain a meaningful interpretation of color features. In this study, we dyed three different types of textured materials in the same bath with a yellow reactive dye at incremental concentrations to see how their color difference profiles tested. Three typical cotton textures were dyed with three ultra-RGB remozol reactive dyes and their combinations. The color concentration ranges of 1%, 2%, 3%, and 4% were chosen for each dye, followed by their binary and ternary mixtures. The aim was to verify the fundamental spectral feature mapping in various imaging color spaces and spectral domains. The findings are quite interesting and help us to understand the ground truth behind working in two domains. In addition, the trends of color mixing, CIE color difference, CIExy (chromaticity) color gamut, and RGB gamut and their distinguishing features were verified. Human perception accuracy was also compared in both domains to clarify the influence of texture. These fundamental experiments and observations on human perception and calibrated imaging color space could clarify the expected precision in both domains. Full article
(This article belongs to the Collection Image and Video Analysis and Understanding)
Show Figures

Figure 1

29 pages, 1470 KiB  
Article
An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction
by Yibing Wang, Liang Zhou, Zhutian Yang, Longwen Wu, Zhendong Yin, Yaqin Zhao and Zhilu Wu
Electronics 2023, 12(9), 2134; https://doi.org/10.3390/electronics12092134 - 06 May 2023
Cited by 2 | Viewed by 1762
Abstract
Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this [...] Read more.
Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this paper, aiming at addressing the issue of poor recognition performance at low signal-to-noise ratios (SNRs) and the inability of deep features to effectively distinguish among all modulation types, we propose an optimization scheme for modulation recognition based on fine-tuning and feature re-extraction. In the proposed scheme, the network is firstly trained with the signals at high SNRs; then, the trained network is fine-tuned to the untrained network at low SNRs. Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. The simulation results demonstrate that the proposed optimization scheme can maximize the performance of the neural network in the recognition of signals that are easily confused and at low SNRs. Notably, the average recognition accuracy of the proposed scheme was 91.28% within an SNR range of −8 dB to 18 dB, which is an improvement of 8% to 17% in comparison with four existing schemes. Full article
(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)
Show Figures

Figure 1

13 pages, 1240 KiB  
Communication
Heavy Ion Induced Degradation Investigation on 4H-SiC JBS Diode with Different P+ Intervals
by Zhikang Wu, Yun Bai, Chengyue Yang, Chengzhan Li, Jilong Hao, Xiaoli Tian, Antao Wang, Yidan Tang, Jiang Lu and Xinyu Liu
Electronics 2023, 12(9), 2133; https://doi.org/10.3390/electronics12092133 - 06 May 2023
Viewed by 1487
Abstract
The heavy ion radiation response and degradation of SiC junction barrier Schottky (JBS) diodes with different P+ implantation intervals (S) are studied in detail. The experimental results show that the larger the S, the faster the reverse leakage current increases, and the more [...] Read more.
The heavy ion radiation response and degradation of SiC junction barrier Schottky (JBS) diodes with different P+ implantation intervals (S) are studied in detail. The experimental results show that the larger the S, the faster the reverse leakage current increases, and the more serious the degradation after the experiment. TCAD simulation shows that the electric field of sensitive points directly affects the degradation rate of devices with different structures. The large transient energy introduced by the heavy ion impact can induce a local temperature increase in the device resulting in lattice damage and the introduction of defects. The reverse leakage current of the degraded device is the same at low voltage as before the experiment, and is gradually dominated by space-charge-limited-conduction (SCLC) as the voltage rises, finally showing ballistic transport characteristics at high voltage. Full article
(This article belongs to the Special Issue New Insights in Radiation-Tolerant Electronics)
Show Figures

Figure 1

17 pages, 5166 KiB  
Article
Arbitrary-Oriented Object Detection in Aerial Images with Dynamic Deformable Convolution and Self-Normalizing Channel Attention
by Yutong Zhang, Chunjie Ma, Li Zhuo and Jiafeng Li
Electronics 2023, 12(9), 2132; https://doi.org/10.3390/electronics12092132 - 06 May 2023
Cited by 3 | Viewed by 1470
Abstract
Objects in aerial images often have arbitrary orientations and variable shapes and sizes. As a result, accurate and robust object detection in aerial images is a challenging problem. In this paper, an arbitrary-oriented object detection method for aerial images, based on Dynamic Deformable [...] Read more.
Objects in aerial images often have arbitrary orientations and variable shapes and sizes. As a result, accurate and robust object detection in aerial images is a challenging problem. In this paper, an arbitrary-oriented object detection method for aerial images, based on Dynamic Deformable Convolution (DDC) and Self-normalizing Channel Attention Mechanism (SCAM), is proposed; this method uses ReResNet-50 as the backbone network to extract rotation-equivariant features. First, DDC is proposed as a replacement for the conventional convolution operation in the Convolutional Neural Network (CNN) in order to cope with various shapes, sizes and arbitrary orientations of the objects. Second, SCAM embedded into the high layer of ReResNet-50, which allows the network to enhance the important feature channels and suppress the irrelevant ones. Finally, Rotation Regions of Interest (RRoI) are generated based on a Region Proposal Network (RPN) and a RoI Transformer (RT), and the RoI-wise classification and bounding box regression are realized by Rotation-invariant RoI Align (RiRoI Align). The proposed method is comprehensively evaluated on three publicly available benchmark datasets. The mean Average Precision (mAP) can reach 80.91%, 92.73% and 94.1% on DOTA-v1.0, DOTA-v1.5 and HRSC2016 datasets, respectively. The experimental results show that, when compared with the state-of-the-arts methods, the proposed method can achieve superior detection accuracy. Full article
(This article belongs to the Special Issue Image and Video Processing Based on Deep Learning)
Show Figures

Figure 1

28 pages, 5774 KiB  
Article
Semantic Knowledge-Based Hierarchical Planning Approach for Multi-Robot Systems
by Sanghyeon Bae, Sunghyeon Joo, Junhyeon Choi, Jungwon Pyo, Hyunjin Park and Taeyong Kuc
Electronics 2023, 12(9), 2131; https://doi.org/10.3390/electronics12092131 - 06 May 2023
Viewed by 1347
Abstract
Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of [...] Read more.
Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed various methods for modeling and planning multi-robot missions. In particular, some approaches have been presented for high-level task planning by introducing semantic knowledge, such as relationships and domain rules, for environmental factors. This paper proposes a semantic knowledge-based hierarchical planning approach for multi-robot systems. We extend the semantic knowledge by considering the influence and interaction between environmental elements in multi-robot systems. Relationship knowledge represents the space occupancy of each environmental element and the possession of objects. Additionally, the knowledge property is defined to express the hierarchical information of each space. Based on the suggested semantic knowledge, the task planner utilizes spatial hierarchy knowledge to group the robots and generate optimal task plans for each group. With this approach, our method efficiently plans complex missions while handling overlap and deadlock problems among the robots. The experiments verified the feasibility of the suggested semantic knowledge and demonstrated that the task planner could reduce the planning time in simulation environments. Full article
(This article belongs to the Special Issue AI in Mobile Robotics)
Show Figures

Figure 1

18 pages, 5253 KiB  
Article
An Electronic Jamming Method Based on a Distributed Information Sharing Mechanism
by Pan Zhang, Yi Huang and Zhonghe Jin
Electronics 2023, 12(9), 2130; https://doi.org/10.3390/electronics12092130 - 06 May 2023
Cited by 1 | Viewed by 1527
Abstract
In an electronic jamming system, the ability to adequately perceive information determines the effectiveness of an electronic countermeasures strategy. This paper proposes a new method based on the combination of a multi-agent electronic jammer and an information sharing mechanism. With the development of [...] Read more.
In an electronic jamming system, the ability to adequately perceive information determines the effectiveness of an electronic countermeasures strategy. This paper proposes a new method based on the combination of a multi-agent electronic jammer and an information sharing mechanism. With the development of intelligent technology and deep learning, these technologies have been applied in electronic countermeasure game systems. Introducing intelligent technology into the electronic confrontation system can greatly improve decision-making efficiency. At the same time, a multi-agent electronic countermeasure cooperative system based on the information sharing method can break through the limited information perception capabilities of a single agent, thereby greatly improving the survivability of jamming systems in electronic warfare. Experimental results show that our method requires a lower jamming-to-signal ratio than the single jammer method to achieve effective electronic jamming. In addition, the electronic jamming parameters can be updated automatically as the external electromagnetic environment changes quickly, realizing a more intelligent electronic jamming system. Full article
Show Figures

Figure 1

15 pages, 1684 KiB  
Article
Multi-Thread Real-Time Control Based on Event-Triggered Mechanism
by Yixuan Wang, Hui Gao, Yang Yang and Pan Xu
Electronics 2023, 12(9), 2129; https://doi.org/10.3390/electronics12092129 - 06 May 2023
Viewed by 768
Abstract
In this paper, we consider the control problem for systems in which the number of controllers does not match that of plants. As the model of the control plants becomes increasingly complex and diversified, a large number of control systems often share one [...] Read more.
In this paper, we consider the control problem for systems in which the number of controllers does not match that of plants. As the model of the control plants becomes increasingly complex and diversified, a large number of control systems often share one or a small number of control centers, and each subsystem cannot receive control signals at the same time. A multi-thread control algorithm based on an event-triggered mechanism is proposed to solve the control problem yielding input-to-state stability and achieve the ability to save communication and computing resources. The concept of multi-threading in the computer field is first used to describe such complex systems in control systems, and the two situations of the continuous updating of the controller and the maintaining of the current state are considered in the modeling. The event-triggered rule of the multi-thread control system is designed using the constraint relationship between the errors and the state functions. The feasibility of the proposed algorithm is ensured by its ability to avoid the Zeno phenomenon that may occur in the controller switching process. Finally, the proposed algorithm is investigated using simulations, showing that it has excellent flexibility, robustness, and practicability. Full article
Show Figures

Figure 1

24 pages, 685 KiB  
Article
Blockchain-Enabled IoT for Rural Healthcare: Hybrid-Channel Communication with Digital Twinning
by Steve Kerrison, Jusak Jusak and Tao Huang
Electronics 2023, 12(9), 2128; https://doi.org/10.3390/electronics12092128 - 06 May 2023
Cited by 5 | Viewed by 2666
Abstract
Internet of Things (IoT) and blockchains are enabling technologies for modern healthcare applications, offering the improved monitoring of patient health and higher data integrity guarantees. However, in rural settings, communication reliability can pose a challenge that constrains real-time data usage. Additionally, the limited [...] Read more.
Internet of Things (IoT) and blockchains are enabling technologies for modern healthcare applications, offering the improved monitoring of patient health and higher data integrity guarantees. However, in rural settings, communication reliability can pose a challenge that constrains real-time data usage. Additionally, the limited computation and communication resources of IoT sensors also means that they may not participate directly in blockchain transactions, reducing trust. This paper proposes a solution to these challenges, enabling the use of blockchain-based IoT healthcare devices in low-bandwidth rural areas. This integrated system, named hybrid channel healthcare chain (HC2), uses two communication channels: short-range communication for device authorisation and bulk data transfer, and long-range the radio for light-weight monitoring and event notifications. Both channels leverage the same cryptographic identity information, and through the use of a cloud-based digital twin, the IoT device is able to sign its own transactions, without disclosing the key to said twin. Patient data are encrypted end to end between the IoT device and data store, with the blockchain providing a reliable record of the data lifecycle. We contribute a model, analytic evaluation and proof of concept for the HC2 system that demonstrates its suitability for the stated scenarios by reducing the number of long-range radio packets needed by 87× compared to a conventional approach. Full article
(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems)
Show Figures

Figure 1

16 pages, 4537 KiB  
Article
Assessing Impedance Analyzer Data Quality by Fractional Order Calculus: A QCM Sensor Case Study
by Ioan Burda
Electronics 2023, 12(9), 2127; https://doi.org/10.3390/electronics12092127 - 06 May 2023
Cited by 1 | Viewed by 957
Abstract
The paper presents the theoretical, simulation, and experimental results on the QCM sensor based on the Butterworth van Dyke (BVD) model with lumped reactive motional circuit elements of fractional order. The equation of the fractional order BVD model of the QCM sensor has [...] Read more.
The paper presents the theoretical, simulation, and experimental results on the QCM sensor based on the Butterworth van Dyke (BVD) model with lumped reactive motional circuit elements of fractional order. The equation of the fractional order BVD model of the QCM sensor has been derived based on Caputo definitions and its behavior around the resonant frequencies has been simulated. The simulations confirm the ability of fractional order calculus to cover a wide range of behaviors beyond those found in experimental practice. The fractional order BVD model of the QCM sensor is considered from the perspective of impedance spectroscopy to give an idea of the advantages that fractional order calculus brings to its modeling. For the true values of the electrical parameters of the QCM sensor based on the standard BVD model, the experimental investigations confirm the equivalence of the measurements after the standard compensation of the virtual impedance analyzer (VIA) and the measurements without compensation by fitting with the fractional order BVD model. From an experimental point of view, using fractional order calculus brings a new dimension to impedance analyzer compensation procedures, as well as a new method for validating the compensation. Full article
Show Figures

Figure 1

31 pages, 8630 KiB  
Article
Instance Segmentation of Irregular Deformable Objects for Power Operation Monitoring Based on Multi-Instance Relation Weighting Module
by Weihao Chen, Lumei Su, Zhiwei Lin, Xinqiang Chen and Tianyou Li
Electronics 2023, 12(9), 2126; https://doi.org/10.3390/electronics12092126 - 06 May 2023
Viewed by 1496
Abstract
Electric power operation is necessary for the development of power grid companies, where the safety monitoring of electric power operation is difficult. Irregular deformable objects commonly used in electrical construction, such as safety belts and seines, have a dynamic geometric appearance which leads [...] Read more.
Electric power operation is necessary for the development of power grid companies, where the safety monitoring of electric power operation is difficult. Irregular deformable objects commonly used in electrical construction, such as safety belts and seines, have a dynamic geometric appearance which leads to the poor performance of traditional detection methods. This paper proposes an end-to-end instance segmentation method using the multi-instance relation weighting module for irregular deformable objects. To solve the problem of introducing redundant background information when using the horizontal rectangular box detector, the Mask Scoring R-CNN is used to perform pixel-level instance segmentation so that the bounding box can accurately surround the irregular objects. Considering that deformable objects in power operation workplaces often appear with construction personnel and the objects have an apparent correlation, a multi-instance relation weighting module is proposed to fuse the appearance features and geometric features of objects so that the relation features between objects are learned end-to-end to improve the segmentation effect of irregular objects. The segmentation mAP on the self-built dataset of irregular deformable objects for electric power operation workplaces reached up to 44.8%. With the same 100,000 training rounds, the bounding box mAP and segmentation mAP improved by 1.2% and 0.2%, respectively, compared with the MS R-CNN. Finally, in order to further verify the generalization performance and practicability of the proposed method, an intelligent monitoring system for the power operation scenes is designed to realize the actual deployment and application of the proposed method. Various tests show that the proposed method can segment irregular deformable objects well. Full article
Show Figures

Figure 1

10 pages, 3307 KiB  
Article
Optimization and Design of Balanced BPF Based on Mixed Electric and Magnetic Couplings
by Qiwei Li, Jinyong Fang, Wen Cao, Jing Sun, Jun Ding, Weihao Tie, Feng Wei, Chang Zhai and Jiangniu Wu
Electronics 2023, 12(9), 2125; https://doi.org/10.3390/electronics12092125 - 06 May 2023
Viewed by 946
Abstract
A balanced bandpass filter (BPF) with an improved frequency selectivity for differential-mode (DM) excitation and high rejection for common-mode (CM) excitation is proposed in this paper. Two half-wavelength stepped impedance resonators (SIRs) are employed based on mixed electric and magnetic couplings to realize [...] Read more.
A balanced bandpass filter (BPF) with an improved frequency selectivity for differential-mode (DM) excitation and high rejection for common-mode (CM) excitation is proposed in this paper. Two half-wavelength stepped impedance resonators (SIRs) are employed based on mixed electric and magnetic couplings to realize a DM passband centered at 2.48 GHz. The center frequency and bandwidth can be easily controlled by optimizing the dimensions of SIRs and the coupling between them, respectively. Meanwhile, two transmission zeros (TZs) are generated based on the mixed electric and magnetic couplings and are independently controlled by tuning the coupling strength. Moreover, a wide DM stopband can be realized by optimizing the SIRs. The proposed balanced BPF is fed by balanced U-type microstrip–slotline transition structures, which can achieve high wideband CM rejection without influencing the DM responses, and the design complexity can be clearly reduced. Finally, a balanced BPF is fabricated, and a good agreement between the simulation and the measurement is observed, which verifies the design method. Full article
Show Figures

Figure 1

15 pages, 735 KiB  
Article
HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network
by Hongwei Zhong, Mingyang Wang and Xinyue Zhang
Electronics 2023, 12(9), 2124; https://doi.org/10.3390/electronics12092124 - 06 May 2023
Cited by 2 | Viewed by 1490
Abstract
Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous [...] Read more.
Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively. Full article
(This article belongs to the Collection Graph Machine Learning)
Show Figures

Figure 1

16 pages, 7799 KiB  
Article
Leakage Current Detector and Warning System Integrated with Electric Meter
by Tsung-Hui Cheng, Chien-Hao Chen, Chien-Hung Lin, Bor-Horng Sheu, Chia-Hung Lin and Wen-Ping Chen
Electronics 2023, 12(9), 2123; https://doi.org/10.3390/electronics12092123 - 06 May 2023
Viewed by 4337
Abstract
Electrical power is essential in human life. Thus, the security and reliability of its supply are of critical importance in a country’s industrial and economic development. The leakage and improper use of electricity may cause serious problems such as fire and electrocution. To [...] Read more.
Electrical power is essential in human life. Thus, the security and reliability of its supply are of critical importance in a country’s industrial and economic development. The leakage and improper use of electricity may cause serious problems such as fire and electrocution. To prevent such incidents and minimize the loss of life and property, a leakage current detector and warning system are developed in this study. With a high-precision current transformer and high-gain linear converter, the detector effectively detects leakage current over 1 mA, which is validated in different methods. The detector can be integrated into widely used electric meters (Taipower Datong’s sub-meter model D4S) easily, and information on detected leakage current is transmitted to the cloud server through narrowband IoT wireless communication (NB-IoT) to warn users and management personnel of the electrical power line. The proposed detector and system are expected to prevent the fire caused by leakage current which was the main cause of the fire at homes and buildings and can be an effective means to manage the electrical powerline system and metering facilities. Full article
Show Figures

Figure 1

15 pages, 5278 KiB  
Article
Admittance Remodeling Strategy of Grid-Connected Inverter Based on Improving GVF
by Shengqing Li, Simin Huang, Weihua He and Dong Zhang
Electronics 2023, 12(9), 2122; https://doi.org/10.3390/electronics12092122 - 06 May 2023
Viewed by 1019
Abstract
With the continuous enhancement of weak grid characteristics, the negative effects of grid voltage feedforward (GVF) and PLL on grid-connected inverters become more and more serious and are coupled. Therefore, it is difficult to effectively solve the system stability problem by only improving [...] Read more.
With the continuous enhancement of weak grid characteristics, the negative effects of grid voltage feedforward (GVF) and PLL on grid-connected inverters become more and more serious and are coupled. Therefore, it is difficult to effectively solve the system stability problem by only improving the PLL structure. Firstly, based on an improved phase-locked loop structure (CCF-PLL) with complex coefficient filter and considering the influence of GVF, the output admittance model of a grid-connected inverter is established. Through stability analysis, it is found that conventional GVF leads the total output admittance phase of the inverter, thus reducing the system stability margin under the weak grid. Then, an improved admittance remodeling strategy of the grid-connected inverter is proposed. An all-pass filter is introduced into the GVF loop to correct the phase of the total output admittance of the inverter, and the phase margin is used as the constraint to design the control parameters, which effectively improves the stability of the system under the weak grid. Finally, the simulation results show that, compared with traditional GVF, the proposed strategy can obviously improve the distortion of grid-connected current waveforms and improve system stability. Full article
(This article belongs to the Special Issue IoT Applications for Renewable Energy Management and Control)
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop