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Electronics, Volume 12, Issue 14 (July-2 2023) – 203 articles

Cover Story (view full-size image): To address the challenges of small antenna gain and narrow frequency range, this study employs a fractal repeating array structure and a double-layer folding antenna design to create a high-gain, flexible, and multi-frequency antenna. Fabricated on a 0.1 mm-thick polyimide substrate, this antenna ingeniously integrates the Minkowski fractal structure as a repeating array unit, combined with the double-layer folding antenna design. The resulting antenna measures a mere 0.04 λ0 × 0.026 λ00 @ 2.4 GHz) and weighs only 4 mg. Moreover, the maximum gains of 1.65 dBi and 4.37 dBi were achieved in two frequency bands. Such remarkable dimensions and excellent performance make it ideal for miniature wireless transmission systems and compact mobile communication devices. View this paper
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14 pages, 3399 KiB  
Article
Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning
by Kun Zhang, Yong Zhang, Junjie Wu and Zhizhong Li
Electronics 2023, 12(14), 3204; https://doi.org/10.3390/electronics12143204 - 24 Jul 2023
Cited by 1 | Viewed by 1074
Abstract
The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as [...] Read more.
The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as image recognition, are vulnerable to weather or light intensity, while microswitch, attitude sensing and other methods are unable to induce equipment power failure with sufficient speed, which brings many new challenges to the operation and maintenance of a GIS. Therefore, this research designs a GIS shell vibration detection system for knife switch state discrimination, introduces a deep learning algorithm for knife switch vibration signal analysis, and proposes a fast convolutional neural network (FCNN) to identify the knife switch state. For the designed FCNN, a normalization layer and a nonlinear activation layer are used after each convolution layer to obviously reduce feature quantity and increase algorithm efficiency. In order to test the recognition performance based on the vibration detection system, this study carried out two kinds of knife switch opening and closing experiments. One group with artificial noise was added, the other group did not include artifical noise, and a corresponding data set was constructed. The experimental results show that the recognition accuracy for both datasets reaches 100%, and the FCNN algorithm is better than the five classical algorithms in terms of prediction efficiency. This study shows that the vibration detection technology based on deep learning can be used to effectively identify the opening and closing state of a GIS knife switch, and is expected to be promoted and applied. Full article
(This article belongs to the Special Issue Recent Advances in Applied Deep Neural Network)
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16 pages, 7560 KiB  
Article
Remote Bridge Inspection and Actual Bridge Verification Based on 4G/5G Communication Environments
by Mai Yoshikura, Takahiro Minami, Tomotaka Fukuoka, Makoto Fujiu and Jyunichi Takayama
Electronics 2023, 12(14), 3203; https://doi.org/10.3390/electronics12143203 - 24 Jul 2023
Viewed by 1011
Abstract
The close-up visual inspection of bridges faces several problems, including a lack of financial resources and human personnel. Hence, there has been increasing use of artificial intelligence (AI) and information and communications technology (ICT) to solve them. We previously investigated remote inspection—in which [...] Read more.
The close-up visual inspection of bridges faces several problems, including a lack of financial resources and human personnel. Hence, there has been increasing use of artificial intelligence (AI) and information and communications technology (ICT) to solve them. We previously investigated remote inspection—in which skilled engineers provided on-site support from a remote location—with the aim of reducing the labor required for on-site work and addressing the lack of personnel through the use of AI and ICT. Sharing images of bridges from inspection sites to remote locations via the Internet enables remote assessment of the sites and the ability to consider and diagnose damage. Mobile communications can be used to upload images, although the volume of image data required for inspection can be enormous and take considerable time to upload. Consequently, in this study, we investigated image uploads using 5G communication—that is, the fifth-generation technology standard for broadband cellular networks. Moreover, we measured the upload times when using 4G and 5G services and examined their operation based on differences in the communication environments. We concluded that the simulated remote inspection can be efficiently performed by adjusting the inspection method to the communication environment. Full article
(This article belongs to the Special Issue Smart Applications of 5G Network)
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22 pages, 10205 KiB  
Article
Research on Road Sign Detection and Visual Depth Perception Technology for Mobile Robots
by Jianwei Zhao and Yushuo Liu
Electronics 2023, 12(14), 3202; https://doi.org/10.3390/electronics12143202 - 24 Jul 2023
Viewed by 965
Abstract
To accomplish the task of detecting and avoiding road signs by mobile robots for autonomous running, in this paper, we propose a method of road sign detection and visual depth perception based on improved Yolov5 and improved centroid depth value filtering. First, the [...] Read more.
To accomplish the task of detecting and avoiding road signs by mobile robots for autonomous running, in this paper, we propose a method of road sign detection and visual depth perception based on improved Yolov5 and improved centroid depth value filtering. First, the Yolov5 model has a large number of parameters, a large computational volume, and a large model size, which is difficult to deploy to the CPU side (industrial control computer) of the robot mobile platform. To solve this problem, the study proposes a lightweight Yolov5-SC3FB model. Compared with the original Yolov5n model, the Yolov5-SC3FB model only loses lower detection accuracy, the parameter volume is reduced to 0.19 M, the computational volume is reduced to 0.5 GFLOPS, and the model size is only 0.72 MB, making it easy to deploy on mobile robot platforms. Secondly, the obtained depth value of the center point of the bounding box is 0 due to the influence of noise. To solve this problem, we proposed an improved filtering method for the depth value of the center point in the study, and the relative error of its depth measurement is only 2%. Finally, the improved Yolov5-SC3FB model is fused with the improved filtering method for acquiring centroid depth values and the fused algorithm is deployed to the mobile robot platform. We verified the effectiveness of this fusion algorithm for the detection and avoidance of road signs of the robot. Thus, it can enable the mobile robot to correctly perceive the environment and achieve autonomous running. Full article
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25 pages, 30967 KiB  
Article
A Decoupled Semantic–Detail Learning Network for Remote Sensing Object Detection in Complex Backgrounds
by Hao Ruan, Wenbin Qian, Zhihong Zheng and Yingqiong Peng
Electronics 2023, 12(14), 3201; https://doi.org/10.3390/electronics12143201 - 24 Jul 2023
Cited by 1 | Viewed by 1079
Abstract
Detecting multi-scale objects in complex backgrounds is a crucial challenge in remote sensing. The main challenge is that the localization and identification of objects in complex backgrounds can be inaccurate. To address this issue, a decoupled semantic–detail learning network (DSDL-Net) was proposed. Our [...] Read more.
Detecting multi-scale objects in complex backgrounds is a crucial challenge in remote sensing. The main challenge is that the localization and identification of objects in complex backgrounds can be inaccurate. To address this issue, a decoupled semantic–detail learning network (DSDL-Net) was proposed. Our proposed approach comprises two components. Firstly, we introduce a multi-receptive field feature fusion and detail mining (MRF-DM) module, which learns higher semantic-level representations by fusing multi-scale receptive fields. Subsequently, it uses multi-scale pooling to preserve detail texture information at different scales. Secondly, we present an adaptive cross-level semantic–detail fusion (CSDF) network that leverages a feature pyramid with fusion between detailed features extracted from the backbone network and high-level semantic features obtained from the topmost layer of the pyramid. The fusion is accomplished through two rounds of parallel global–local contextual feature extraction, with shared learning for global context information between the two rounds. Furthermore, to effectively enhance fine-grained texture features conducive to object localization and features conducive to object semantic recognition, we adopt and improve two enhancement modules with attention mechanisms, making them simpler and more lightweight. Our experimental results demonstrate that our approach outperforms 12 benchmark models on three publicly available remote sensing datasets (DIOR, HRRSD, and RSOD) regarding average precision (AP) at small, medium, and large scales. On the DIOR dataset, our model achieved a 2.19% improvement in mAP@0.5 compared to the baseline model, with a parameter reduction of 14.07%. Full article
(This article belongs to the Topic Computer Vision and Image Processing)
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13 pages, 1083 KiB  
Article
Efficient Overdetermined Independent Vector Analysis Based on Iterative Projection with Adjustment
by Ruiming Guo, Zhongqiang Luo, Ling Wang and Li Feng
Electronics 2023, 12(14), 3200; https://doi.org/10.3390/electronics12143200 - 24 Jul 2023
Viewed by 846
Abstract
In this paper, a computationally efficient optimization algorithm for independent vector analysis (IVA) is proposed to accelerate iterative convergence speed and enhance the overdetermined convolutive blind speech separation performance. An iterative projection with adjustment (IPA) is investigated to estimate the unmixing matrix for [...] Read more.
In this paper, a computationally efficient optimization algorithm for independent vector analysis (IVA) is proposed to accelerate iterative convergence speed and enhance the overdetermined convolutive blind speech separation performance. An iterative projection with adjustment (IPA) is investigated to estimate the unmixing matrix for OverIVA. The IPA algorithm jointly executes the iterative projection (IP) algorithm and the iterative source steering (ISS) algorithm to jointly update one row and one column of the mixing matrix, which can perform computationally-efficient blind source separation. It is achieved by updating one demixing filter and jointly adjusting all the other sources along its current direction. Motivated by its technology superiorities, this paper proposes a modified algorithm for the OverIVA, fully exploiting the computational efficiency of IPA optimization scheme. Experimental results corroborate the proposed OverIVA-IPA algorithm converges faster and performs better than the existing state-of-the-arts algorithms. Full article
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23 pages, 6014 KiB  
Article
Dynamic Health Monitoring of Aero-Engine Gas-Path System Based on SFA-GMM-BID
by Dewen Li, Yang Li, Tianci Zhang, Jing Cai, Hongfu Zuo and Ying Zhang
Electronics 2023, 12(14), 3199; https://doi.org/10.3390/electronics12143199 - 24 Jul 2023
Cited by 1 | Viewed by 1167
Abstract
This paper proposes a dynamic health monitoring method for aero-engines by extracting more hidden information from the raw values of gas-path parameters based on slow feature analysis (SFA) and the Gaussian mixture model (GMM) to improve the capability of detecting gas-path faults of [...] Read more.
This paper proposes a dynamic health monitoring method for aero-engines by extracting more hidden information from the raw values of gas-path parameters based on slow feature analysis (SFA) and the Gaussian mixture model (GMM) to improve the capability of detecting gas-path faults of aero-engines. First, an SFA algorithm is used to process the raw values of gas-path parameters, extracting the effective features reflecting the slow variation of the gas-path state. Then, a GMM is established based on the slow features of the target aero-engine in a normal state to measure its health status. Moreover, an indicator based on the Bayesian inference distance (BID) is constructed to quantitatively characterize the performance degradation degree of the target aero-engine. Considering that the fixed threshold does not suit the time-varying characteristics of the gas-path state, a dynamic threshold based on the maximum information coefficient is designed for aero-engine health monitoring. The proposed method is verified using a set of actual operation data of a certain aero-engine. The results show that the proposed method can better reflect the degradation process of the aero-engine and identify aero-engine anomalies earlier than other aero-engine fault detection methods. In addition, the dynamic threshold can reduce the occurrence of false alarms. All these advantages give the proposed method high value in real-world applications. Full article
(This article belongs to the Section Computer Science & Engineering)
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10 pages, 2488 KiB  
Article
Soft-Error-Aware Radiation-Hardened Ge-DLTFET-Based SRAM Cell Design
by Pushpa Raikwal, Prashant Kumar, Meena Panchore, Pushpendra Dwivedi and Kanchan Cecil
Electronics 2023, 12(14), 3198; https://doi.org/10.3390/electronics12143198 - 24 Jul 2023
Viewed by 1035
Abstract
In this paper, a soft-error-aware radiation-hardened 6T SRAM cell has been implemented using germanium-based dopingless tunnel FET (Ge DLTFET). In a circuit level simulation, the device-circuit co-design approach is used. Semiconductor devices are very prone to the radiation environment; hence, finding out the [...] Read more.
In this paper, a soft-error-aware radiation-hardened 6T SRAM cell has been implemented using germanium-based dopingless tunnel FET (Ge DLTFET). In a circuit level simulation, the device-circuit co-design approach is used. Semiconductor devices are very prone to the radiation environment; hence, finding out the solution to the problem became a necessity for the designers. Single event upset (SEU), also known as soft error, is one of the most frequent issues to tackle in semiconductor devices. To mitigate the effect of soft error due to single-event upset, the radiation-hardening-by-design (RHBD) technique has been employed for Ge DLTFET-based SRAM cells. This technique uses RC feedback paths between the two cross-coupled inverters of an SRAM cell. The soft-error sensitivity is estimated for a conventional and RHBD-based SRAM cell design. It is found that the RHBD-based SRAM cell design is more efficient to mitigate the soft-error effect in comparison to the conventional design. The delay and stability parameters, obtained from the N-curve, of the Ge DLTFET-based SRAM cell performs better than the conventional Si TFET-based SRAM cell. There is an improvement of 305x & 850x in the static power noise margin and write trip power values of the Ge DLTFET SRAM cell with respect to the conventional Si TFET SRAM cell. Full article
(This article belongs to the Special Issue Advanced CMOS Devices)
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1 pages, 157 KiB  
Retraction
RETRACTED: Obodoekwe et al. Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy. Electronics 2022, 11, 2128
by Ekene Obodoekwe, Xianwen Fang and Ke Lu
Electronics 2023, 12(14), 3197; https://doi.org/10.3390/electronics12143197 - 24 Jul 2023
Viewed by 661
Abstract
The journal retracts the article entitled “Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy” [...] Full article
(This article belongs to the Section Computer Science & Engineering)
15 pages, 9756 KiB  
Communication
A Low Jitter, Wideband Clock Generator for Multi-Protocol Data Communications Applications
by Yingdan Jiang, Yang Yu, Lu Tang, Junhao Yang, Yujia Lu and Zongguang Yu
Electronics 2023, 12(14), 3196; https://doi.org/10.3390/electronics12143196 - 24 Jul 2023
Viewed by 932
Abstract
This paper presents a charge-pump phase-locked loop (PLL) frequency-synthesizer-based low-jitter wideband clock generator for multi-protocol data communications applications. Automatic frequency calibration (AFC) using linear variable time window technology and modified multi-modulus dividers (MMD) based on sub-multi-modulus dividers (SMMD) are developed for faster locking, [...] Read more.
This paper presents a charge-pump phase-locked loop (PLL) frequency-synthesizer-based low-jitter wideband clock generator for multi-protocol data communications applications. Automatic frequency calibration (AFC) using linear variable time window technology and modified multi-modulus dividers (MMD) based on sub-multi-modulus dividers (SMMD) are developed for faster locking, lower jitter, and implementation of multi-protocol data communications applications. The clock generator is fabricated in 0.18 μm CMOS technology. The measured division ratio of the multi-modulus divider ranges from 1.875 to 25, and the output frequency is 46.875~625 MHz. The lock time does not exceed 30 μs, while jitter is less than 500 fs. Full article
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13 pages, 2816 KiB  
Article
Blockchain for Healthcare Games Management
by Sheng Chen, Qi Cao and Yiyu Cai
Electronics 2023, 12(14), 3195; https://doi.org/10.3390/electronics12143195 - 24 Jul 2023
Cited by 2 | Viewed by 1132
Abstract
More and more serious games have been developed in recent years for patients with aging, rehabilitation, mental, and other healthcare needs. Often, such healthcare games have different experiments or evaluations associated. Rapid advancements in healthcare games see increased regulatory concerns. Unfortunately, there is [...] Read more.
More and more serious games have been developed in recent years for patients with aging, rehabilitation, mental, and other healthcare needs. Often, such healthcare games have different experiments or evaluations associated. Rapid advancements in healthcare games see increased regulatory concerns. Unfortunately, there is no authority like the Food and Drug Administration (FDA) in United States for regulatory approvals of healthcare games. Yet, it is not appropriate to use the traditional pharmaceutical FDA approval, which is a tedious and time-consuming process, for healthcare games. We propose a Healthcare Game Blockchain (HGB) to support game developers, healthcare providers, healthcare authorities, and patients. For healthcare game developers, HGB is a common platform to deposit healthcare game prototypes or products. HGB can collect feedback data for analysis similar to drug clinical trials on efficacy and toxicity. Volunteers can be recruited for clinical trials using the healthcare games available on HGB. Once the healthcare games are accepted with a recommended usage, patient gamers can leverage the same HGB platform for treatments following the instructions of healthcare providers. HGB allows healthcare professionals to track the patient responses during their healthcare game medication. Big data analytics can be developed to monitor patient outcomes and healthcare game efficacy. Security and privacy issues are critical in healthcare data handling. HGB has the potential to resolve these limitations and inefficiencies improving data collection, data sharing, and data security by leveraging on the intrinsic properties that blockchain technology provides. This paper explores the possibility of integrating blockchain and healthcare games supporting clinical trials and treatment monitoring. Full article
(This article belongs to the Section Computer Science & Engineering)
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16 pages, 3248 KiB  
Article
Three-Dimensional Measurement of Full Profile of Steel Rail Cross-Section Based on Line-Structured Light
by Jiajia Liu, Jiapeng Zhang, Zhongli Ma, Hangtian Zhang and Shun Zhang
Electronics 2023, 12(14), 3194; https://doi.org/10.3390/electronics12143194 - 24 Jul 2023
Cited by 1 | Viewed by 1146
Abstract
The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the [...] Read more.
The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the measurement plane caused by the vibration of the railcar results in changes in the actual measured rail profile relative to its cross-sectional profile, ultimately leading to measurement deviations. To address these issues, this paper proposes a method for three-dimensional measurement of steel rail cross-sectional profiles based on binocular line-structured light. Firstly, calibrated dual cameras are used to simultaneously capture the profiles of both sides of the steel rail in the same world coordinate system, forming the complete rail profile. Then, considering that the wear at the rail waist is zero in actual operation, the coordinate of the circle center on both sides of the rail waist are connected to form feature vectors. The measured steel rail profile is aligned with the corresponding feature vectors of the standard steel rail model to achieve initial registration; next, the rail profile that has completed the preliminary matching is accurately matched with the target model based on the iterative closest point (ICP) algorithm. Finally, by comparing the projected complete rail profile onto the rail cross-sectional plane with the standard 3D rail model, the amount of wear on the railhead can be obtained. The experimental results indicate that the proposed line-structured-light measurement method for the complete rail profile, when compared to the measurements obtained from the rail wear gauge, exhibits smaller mean absolute deviation (MAD) and root mean square error (RMSE) for both the vertical and lateral dimensions. The MAD values for the vertical and lateral measurements are 0.009 mm and 0.039 mm, respectively, while the RMSE values are 0.011 mm and 0.048 mm. The MAD and RMSE values for the vertical and lateral wear measurements are lower than those obtained using the standard two-dimensional rail profile measurement method. Furthermore, it effectively eliminates the impact of vibrations during the dynamic measurement process, showcasing its practical engineering application value. Full article
(This article belongs to the Special Issue Applications of Computer Vision, Volume II)
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19 pages, 5812 KiB  
Article
Cross-Modality Person Re-Identification Algorithm Based on Two-Branch Network
by Jianfeng Song, Jin Yang, Chenyang Zhang and Kun Xie
Electronics 2023, 12(14), 3193; https://doi.org/10.3390/electronics12143193 - 24 Jul 2023
Viewed by 1092
Abstract
Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible modality is not suitable for low-light environments or at [...] Read more.
Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible modality is not suitable for low-light environments or at night, when crime is frequent. In contrast, infrared images can reflect the nighttime environment, and most surveillance systems are equipped with dual-mode cameras that can automatically switch between visible and infrared modalities based on light conditions. In contrast to visible-light cameras, infrared (IR) cameras can still capture enough information from the scene in those dark environments. Therefore, the problem of visible-infrared cross-modality person re-identification (VI-ReID) is proposed. To improve the identification rate of cross-modality person re-identification, a cross-modality person re-identification method based on a two-branch network is proposed. Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. The effectiveness of the proposed method is verified by extensive experimental analysis, and the method achieves good recognition accuracy on the visible-infrared image person re-identification standard dataset SYSU-MM01. Full article
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17 pages, 2530 KiB  
Article
A Multi-Path Inpainting Forensics Network Based on Frequency Attention and Boundary Guidance
by Hongquan Wang, Xinshan Zhu, Hao Sun, Tongyu Qian and Ying Chen
Electronics 2023, 12(14), 3192; https://doi.org/10.3390/electronics12143192 - 24 Jul 2023
Viewed by 731
Abstract
With the continuous advancement of image-editing technologies, it is particularly important to develop image forensics methods for digital information security. In this study, a deep neural network called multi-path inpainting forensics network (MPIF-Net) was developed to locate the inpainted regions in an image. [...] Read more.
With the continuous advancement of image-editing technologies, it is particularly important to develop image forensics methods for digital information security. In this study, a deep neural network called multi-path inpainting forensics network (MPIF-Net) was developed to locate the inpainted regions in an image. The interaction of shallow and deep features between different paths was established, which not only preserved detailed information but also allowed for the further mining of deep features. Meanwhile, an improved residual dense block was employed as the deep feature extraction module of each path, which can enhance the feature extraction ability of the model by introducing a frequency domain attention mechanism. In addition, a boundary guidance module was constructed to alleviate the prediction distortion in the boundaries of the inpainted region. Finally, extensive experimental results regarding various deep inpainting datasets demonstrated that the proposed network can accurately locate inpainted regions, exhibit excellent generalization and robustness, and verify the effectiveness of the designed module. Full article
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22 pages, 448 KiB  
Article
Defense and Attack Game Strategies of Dual-Network Coupled CPPS with Communication Edge Failures
by Guopeng Zhu, Qiusheng Yu, Shenyang Xiao, Shaobo Qian, Guangming Han, Yan Zhang and Piming Ma
Electronics 2023, 12(14), 3191; https://doi.org/10.3390/electronics12143191 - 24 Jul 2023
Cited by 1 | Viewed by 826
Abstract
With the development of power technology and communication technology, the power grid and power communication network have become interdependent and closely coupled. The load shedding operation of the power grid is an important means to reduce the occurrence of chain faults and ensure [...] Read more.
With the development of power technology and communication technology, the power grid and power communication network have become interdependent and closely coupled. The load shedding operation of the power grid is an important means to reduce the occurrence of chain faults and ensure the safe and stable operation of the power grid. Based on the transmission of load control services in the communication network, this paper establishes a model for a dual-network coupled cyber physical power system (CPPS). Considering communication edge faults, the associated load capacity of the communication edge and the expected load loss of the power grid are defined. On this basis, the paper proposes a complete information zero-sum game mechanism called “defense attack defense” for communication edge failures, which takes the expected loss of load from the power grid as the benefit. The paper studies the optimal attack and defense game strategies and provides the algorithm implementation process for the three stages of the game. Considering the bandwidth capacity of the communication edge, this paper uses the Dijkstra algorithm or k shortest paths (KSP) algorithm with the cost factor of the communication edge as the weight to plan the main and backup communication channels for multiple load control services. The simulation results show that the game mechanism proposed in this paper can effectively reduce the expected load loss from the power grid and improve the stability of the CPPS. Full article
(This article belongs to the Special Issue Advancement in Power Electronics and Control)
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16 pages, 2790 KiB  
Article
Enhancing System Reliability and Transient Voltage Stability through Optimized Power Sources and Network Planning
by Fan Li, Dong Liu, Dan Wang, Wei Wang, Zhongjian Liu, Haoyang Yu, Xiaofan Su, De Zhang and Xiaoman Wu
Electronics 2023, 12(14), 3190; https://doi.org/10.3390/electronics12143190 - 23 Jul 2023
Viewed by 1051
Abstract
Renewable energy is an important means of addressing climate change and achieving carbon peaking and carbon neutrality goals. However, the uncertainty and randomness of renewable energy also have a certain impact on the flexibility, reliability, and transient voltage stability of the power system. [...] Read more.
Renewable energy is an important means of addressing climate change and achieving carbon peaking and carbon neutrality goals. However, the uncertainty and randomness of renewable energy also have a certain impact on the flexibility, reliability, and transient voltage stability of the power system. These effects also pose great challenges to power system planning. In order to address the impact of renewable energy on power system planning, this paper proposes a two-layer optimization model for power sources and network planning which takes into account both reliability and transient voltage stability requirements. The upper-layer grid planning problem is formulated with consideration of the system reliability index, and the transient stability requirements and construction and operation costs are included in the lower-layer problem to determine a construction scheme for power generation and energy storage units. To solve the complex nonlinear problem efficiently, a two-layer iterative algorithm utilizing the adaptive particle swarm optimization (PSO) technique is proposed. The effectiveness of the proposed method is demonstrated via its application to the IEEE 33 test system. The results show that the proposed optimization approach effectively addresses the power system transmission and generation planning problem while improving the efficiency and reliability of the system’s operation. The findings can guide the design and implementation of future power system planning and operation strategies. Full article
(This article belongs to the Special Issue AI-Based Power System Stability and Control Analysis)
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12 pages, 2527 KiB  
Article
Designs of Array Multipliers with an Optimized Delay in Quantum-Dot Cellular Automata
by Aibin Yan, Xuehua Li, Runqi Liu, Zhengfeng Huang, Patrick Girard and Xiaoqing Wen
Electronics 2023, 12(14), 3189; https://doi.org/10.3390/electronics12143189 - 23 Jul 2023
Cited by 2 | Viewed by 1549
Abstract
Quantum-dot cellular automata (QCA) has been considered as a novel nano-electronic technology. With the advantages of low power consumption, high speed, and high integration, QCA has been treated as the potential replacement technology of the CMOS (complementary metal oxide semiconductor) which is currently [...] Read more.
Quantum-dot cellular automata (QCA) has been considered as a novel nano-electronic technology. With the advantages of low power consumption, high speed, and high integration, QCA has been treated as the potential replacement technology of the CMOS (complementary metal oxide semiconductor) which is currently used in the industry. This paper presents a QCA-based array multiplier with an optimized delay. This type of circuit is the basic building block of many arithmetic logic units and electronic communication systems. Compared to the existing array multipliers, the proposed multipliers have the smallest cell count and area. The proposed designs used a compact clock scheme to reduce the carry delay of the signals. The 2 × 2 array multiplier clock delay was reduced by almost 65% compared to the existing designs. Moreover, since the multiplier exhibits a good scalability, for further proof, we proposed a 3 × 3 array multiplier. Simulation results asserted the feasibility of the proposed multipliers. Extensive comparison results demonstrated that when the design scaling was increased, our proposed designs still displayed an efficient overhead in terms of the delay, cell count, and area. The QCADesigner tool was employed to validate the proposed array multipliers. The QCADesigner-E was used to measure the power dissipation of the alternative compared solutions. Full article
(This article belongs to the Special Issue Advances in Analog and Mixed-Signal Integrated Circuits)
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16 pages, 3532 KiB  
Article
A Model for EEG-Based Emotion Recognition: CNN-Bi-LSTM with Attention Mechanism
by Zhentao Huang, Yahong Ma, Rongrong Wang, Weisu Li and Yongsheng Dai
Electronics 2023, 12(14), 3188; https://doi.org/10.3390/electronics12143188 - 22 Jul 2023
Cited by 6 | Viewed by 3677
Abstract
Emotion analysis is the key technology in human–computer emotional interaction and has gradually become a research hotspot in the field of artificial intelligence. The key problems of emotion analysis based on EEG are feature extraction and classifier design. The existing methods of emotion [...] Read more.
Emotion analysis is the key technology in human–computer emotional interaction and has gradually become a research hotspot in the field of artificial intelligence. The key problems of emotion analysis based on EEG are feature extraction and classifier design. The existing methods of emotion analysis mainly use machine learning and rely on manually extracted features. As an end-to-end method, deep learning can automatically extract EEG features and classify them. However, most of the deep learning models of emotion recognition based on EEG still need manual screening and data pre-processing, and the accuracy and convenience are not high enough. Therefore, this paper proposes a CNN-Bi-LSTM-Attention model to automatically extract the features and classify emotions based on EEG signals. The original EEG data are used as input, a CNN and a Bi-LSTM network are used for feature extraction and fusion, and then the electrode channel weights are balanced through the attention mechanism layer. Finally, the EEG signals are classified to different kinds of emotions. An emotion classification experiment based on EEG is conducted on the SEED dataset to evaluate the performance of the proposed model. The experimental results show that the method proposed in this paper can effectively classify EEG emotions. The method was assessed on two distinctive classification tasks, one with three and one with four target classes. The average ten-fold cross-validation classification accuracy of this method is 99.55% and 99.79%, respectively, corresponding to three and four classification tasks, which is significantly better than the other methods. It can be concluded that our method is superior to the existing methods in emotion recognition, which can be widely used in many fields, including modern neuroscience, psychology, neural engineering, and computer science as well. Full article
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20 pages, 4433 KiB  
Article
Maintain a Better Balance between Performance and Cost for Image Captioning by a Size-Adjustable Convolutional Module
by Yan Lyu, Yong Liu and Qiangfu Zhao
Electronics 2023, 12(14), 3187; https://doi.org/10.3390/electronics12143187 - 22 Jul 2023
Viewed by 1207
Abstract
Image captioning is a challenging AI problem that connects computer vision and natural language processing. Many deep learning (DL) models have been proposed in the literature for solving this problem. So far, the primary concern of image captioning has been focused on increasing [...] Read more.
Image captioning is a challenging AI problem that connects computer vision and natural language processing. Many deep learning (DL) models have been proposed in the literature for solving this problem. So far, the primary concern of image captioning has been focused on increasing the accuracy of generating human-style sentences for describing given images. As a result, state-of-the-art (SOTA) models are often too expensive to be implemented in computationally weak devices. In contrast, the primary concern of this paper is to maintain a balance between performance and cost. For this purpose, we propose using a DL model pre-trained for object detection to encode the given image so that features of various objects can be extracted simultaneously. We also propose adding a size-adjustable convolutional module (SACM) before decoding the features into sentences. The experimental results show that the model with the properly adjusted SACM could reach a BLEU-1 score of 82.3 and a BLEU-4 score of 43.9 on the Flickr 8K dataset, and a BLEU-1 score of 83.1 and a BLEU-4 score of 44.3 on the MS COCO dataset. With the SACM, the number of parameters is decreased to 108M, which is about 1/4 of the original YOLOv3-LSTM model with 430M parameters. Specifically, compared with mPLUG with 510M parameters, which is one of the SOTA methods, the proposed method can achieve almost the same BLEU-4 scores, but the number of parameters is 78% less than the mPLUG. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Based Pattern Recognition)
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16 pages, 4007 KiB  
Article
Hybrid Model Predictive Control with Penalty Factor Based on Image-Based Visual Servoing for Constrained Mobile Robots
by Haojie Gu, Qiuyue Qin, Jingfeng Mao, Xingjian Sun and Yuxu Huang
Electronics 2023, 12(14), 3186; https://doi.org/10.3390/electronics12143186 - 22 Jul 2023
Viewed by 860
Abstract
For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive [...] Read more.
For the constrained mobile robot automatic parking system, the hybrid model predictive control with a penalty factor based on image-based visual servoing (IBVS) is proposed to address the problem of feature point loss and emergency braking in dynamic obstacle scenarios caused by excessive target bias gain when using traditional IBVS control methods. The traditional IBVS control is transformed into an optimization problem with constraints in the finite time domain, by defining the optimization function based on the mobile robot’s positional deviation and image feature point deviation, while using actuator saturation and speed limit as constraints. Based on this, a convex optimization function with penalty factors is defined and combined with incremental model predictive control. This control strategy could ensure the emergency braking performance of the mobile robot when the image feature points are massively obscured by obstacles in dynamic scenes, while improving the accuracy and real-time of its trajectory tracking control. Finally, simulation comparisons are conducted to verify the effectiveness of the proposed control method. Full article
(This article belongs to the Section Systems & Control Engineering)
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18 pages, 3232 KiB  
Article
Vulnerability Identification and Assessment for Critical Infrastructures in the Energy Sector
by Nikolaos Nikolaou, Andreas Papadakis, Konstantinos Psychogyios and Theodore Zahariadis
Electronics 2023, 12(14), 3185; https://doi.org/10.3390/electronics12143185 - 22 Jul 2023
Cited by 1 | Viewed by 1659
Abstract
Vulnerability identification and assessment is a key process in risk management. While enumerations of vulnerabilities are available, it is challenging to identify vulnerability sets focused on the profiles and roles of specific organizations. To this end, we have employed systematized knowledge and relevant [...] Read more.
Vulnerability identification and assessment is a key process in risk management. While enumerations of vulnerabilities are available, it is challenging to identify vulnerability sets focused on the profiles and roles of specific organizations. To this end, we have employed systematized knowledge and relevant standards (including National Electric Sector Cybersecurity Organization Resource (NESCOR), ISO/IEC 27005:2018 and National Vulnerability Database (NVD)) to identify a set of 250 vulnerabilities for operators of energy-related critical infrastructures. We have elaborated a “double-mapping” scheme to associate (arbitrarily) categorized assets, with the pool of identified Physical, Cyber and Human/Organizational vulnerabilities. We have designed and implemented an extensible vulnerability identification and assessment framework, allowing historized assessments, based on the CVSS (Common Vulnerability Scoring System) scoring mechanism. This framework has been extended to allow modelling of the vulnerabilities and assessments using the Structured Threat Information eXpression (STIX) JSON format, as Cyber Threat Intelligence (CTI) information, to facilitate information sharing between Electrical Power and Energy Systems (EPES) and to promote collaboration and interoperability scenarios. Vulnerability assessments from the initial analysis of the project in the context of Research and Technology Development (RTD) projects have been statistically processed, offering insights in terms of the assessment’s importance and distribution. The assessments have also been transformed into a dynamic dataset processed to identify and quantify correlation and start the discussion on the interpretation of the way assessments are performed. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence)
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15 pages, 748 KiB  
Article
Performance Optimization of Multipair Massive MIMO Polarized Relay Systems
by Sian Xiong, Zhipeng Chen, Nan Jiang, Jiahui Zhao and Lingfeng Liu
Electronics 2023, 12(14), 3184; https://doi.org/10.3390/electronics12143184 - 22 Jul 2023
Cited by 1 | Viewed by 797
Abstract
In this paper, we analyze the challenges faced by multipair massive Multiple-input-multiple-output (MIMO) relay channels in 5G wireless communication systems, where high path loss and severe shadow fading between different user nodes can cause poor channel quality. To overcome these limitations, we propose [...] Read more.
In this paper, we analyze the challenges faced by multipair massive Multiple-input-multiple-output (MIMO) relay channels in 5G wireless communication systems, where high path loss and severe shadow fading between different user nodes can cause poor channel quality. To overcome these limitations, we propose a polarization selection scheme for antenna arrays combining multipair massive MIMO relay and beamforming to improve MIMO relay channel quality. Specifically, our main goal is to exploit the potential of polarization diversity to maintain and improve the link quality while maintaining the compact size of the MIMO antenna array. To achieve this goal, we introduce a dynamic weighted particle swarm optimization algorithm with contraction factor (CF-DWPSO) to select the polarization direction. In addition, we employ distributed beamforming to effectively suppress or eliminate inter-pair interference. The performance of the simulation analysis shows that CF-DWPSO combined with beamforming provides significant performance improvement of the multipair massive MIMO polarized relay channels, which further indicates that it is of great necessity for improving the performance of this system to optimize the polarization selection by combining beamforming techniques. Full article
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20 pages, 6136 KiB  
Article
ClueReader: Heterogeneous Graph Attention Network for Multi-Hop Machine Reading Comprehension
by Peng Gao, Feng Gao, Peng Wang, Jian-Cheng Ni, Fei Wang and Hamido Fujita
Electronics 2023, 12(14), 3183; https://doi.org/10.3390/electronics12143183 - 22 Jul 2023
Cited by 1 | Viewed by 952
Abstract
Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities and lead to competitive results. However, the analysis and reasoning [...] Read more.
Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities and lead to competitive results. However, the analysis and reasoning of some are inconsistent with those of humans. Inspired by the concept of grandmother cells in cognitive neuroscience, we propose a heterogeneous graph attention network model named ClueReader to imitate the grandmother cell concept. The model is designed to assemble the semantic features in multi-level representations and automatically concentrate or alleviate information for reasoning through the attention mechanism. The name ClueReader is a metaphor for the pattern of the model: it regards the subjects of queries as the starting points of clues, takes the reasoning entities as bridge points, considers the latent candidate entities as grandmother cells, and the clues end up in candidate entities. The proposed model enables the visualization of the reasoning graph, making it possible to analyze the importance of edges connecting entities and the selectivity in the mention and candidate nodes, which is easier to comprehend empirically. Evaluations on the open-domain multi-hop reading dataset WikiHop and drug–drug interaction dataset MedHop proved the validity of ClueReader and showed the feasibility of its application of the model in the molecular biology domain. Full article
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20 pages, 806 KiB  
Article
Vertical Federated Unlearning on the Logistic Regression Model
by Zihao Deng, Zhaoyang Han, Chuan Ma, Ming Ding, Long Yuan, Chunpeng Ge and Zhe Liu
Electronics 2023, 12(14), 3182; https://doi.org/10.3390/electronics12143182 - 22 Jul 2023
Cited by 1 | Viewed by 1504
Abstract
Vertical federated learning is designed to protect user privacy by building local models over disparate datasets and transferring intermediate parameters without directly revealing the underlying data. However, the intermediate parameters uploaded by participants may memorize information about the training data. With the recent [...] Read more.
Vertical federated learning is designed to protect user privacy by building local models over disparate datasets and transferring intermediate parameters without directly revealing the underlying data. However, the intermediate parameters uploaded by participants may memorize information about the training data. With the recent legislation on the“right to be forgotten”, it is crucial for vertical federated learning systems to have the ability to forget or remove previous training information of any client. For the first time, this work fills in this research gap by proposing a vertical federated unlearning method on logistic regression model. The proposed method is achieved by imposing constraints on intermediate parameters during the training process and then subtracting target client updates from the global model. The proposed method boasts the advantages that it does not need any new clients for training and requires only one extra round of updates to recover the performance of the previous model. Moreover, data-poisoning attacks are introduced to evaluate the effectiveness of the unlearning process. The effectiveness of the method is demonstrated through experiments conducted on four benchmark datasets. Compared to the conventional unlearning by retraining from scratch, the proposed unlearning method has a negligible decrease in accuracy but can improve training efficiency by over 400%. Full article
(This article belongs to the Special Issue Security and Privacy Evaluation of Machine Learning in Networks)
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23 pages, 1394 KiB  
Article
Edge-Computing-Enabled Low-Latency Communication for a Wireless Networked Control System
by Daniel Poul Mtowe and Dong Min Kim
Electronics 2023, 12(14), 3181; https://doi.org/10.3390/electronics12143181 - 22 Jul 2023
Cited by 1 | Viewed by 1558
Abstract
This study proposes a novel strategy for enhancing low-latency control performance in Wireless Networked Control Systems (WNCSs) through the integration of edge computing. Traditional networked control systems require the receipt of raw data from remote sensors to enable the controller to generate an [...] Read more.
This study proposes a novel strategy for enhancing low-latency control performance in Wireless Networked Control Systems (WNCSs) through the integration of edge computing. Traditional networked control systems require the receipt of raw data from remote sensors to enable the controller to generate an appropriate control command, a process that can result in substantial periodic communication traffic and consequent performance degradation in some applications. To counteract this, we suggest the use of edge computing to preprocess the raw data, extract the essential features, and subsequently transmit them. Additionally, we introduce an adaptive scheme designed to curtail frequent data traffic by adaptively modifying periodic data transmission based on necessity. This scheme is achieved by refraining from data transmission when a comparative analysis of the previously transmitted and newly generated data shows no significant change. The effectiveness of our proposed strategy is empirically validated through experiments conducted on a remote control system testbed using a mobile robot that navigates the road by utilizing camera information. Through leveraging edge computing, only 3.42% of the raw data was transmitted. Our adaptive scheme reduced the transmission frequency by 20%, while maintaining an acceptable control performance. Moreover, we conducted a comparative analysis between our proposed solution and the state-of-the-art communication framework, WebRTC technology. The results demonstrate that our method effectively reduces the latency by 58.16% compared to utilizing the WebRTC alone in a 5G environment. The experimental results confirm that our proposed strategy significantly improves the latency performance of a WNCS. Full article
(This article belongs to the Special Issue Emerging Trends and Challenges in IoT Networks)
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14 pages, 424 KiB  
Article
Graph Embedding-Based Money Laundering Detection for Ethereum
by Jiayi Liu, Changchun Yin, Hao Wang, Xiaofei Wu, Dongwan Lan, Lu Zhou and Chunpeng Ge
Electronics 2023, 12(14), 3180; https://doi.org/10.3390/electronics12143180 - 21 Jul 2023
Cited by 4 | Viewed by 2392
Abstract
The number of money laundering crimes for Ethereum and the amount involved have grown exponentially in recent years. However, previous studies related to anomaly detection for Ethereum usually consider multiple types of financial crimes as a whole, ignoring the apparent differences between money [...] Read more.
The number of money laundering crimes for Ethereum and the amount involved have grown exponentially in recent years. However, previous studies related to anomaly detection for Ethereum usually consider multiple types of financial crimes as a whole, ignoring the apparent differences between money laundering and other malicious activities and lacking a more granular detection targeting money laundering. In this paper, for the first time, we propose an improved graph embedding algorithm specifically for money laundering detection called GTN2vec. By mining Ethereum transaction records, the algorithm comprehensively considers the behavioral patterns of money launderers and structural information of transaction networks and can automatically extract features of money laundering addresses. Specifically, we fuse the gas price and timestamp from the transaction records into a new weight and set appropriate return and exploration parameters to modulate the sampling tendency of random walk to characterize the money laundering nodes. We construct the dataset using real Ethereum data and evaluate the effectiveness of GTN2vec on the dataset by various classifiers such as random forest. The experimental results show that GTN2vec can accurately and effectively extract money laundering account features and significantly outperform other advanced graph embedding methods. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems)
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23 pages, 3182 KiB  
Article
Collision Avoidance Second Order Sliding Mode Control of Satellite Formation with Air-Floated Platform Semi-Physical Simulation
by Ji Zhang, Yili Wang, Jun Jia, Chuanguo Chi and Huayi Li
Electronics 2023, 12(14), 3179; https://doi.org/10.3390/electronics12143179 - 21 Jul 2023
Cited by 1 | Viewed by 966
Abstract
As the number of satellites in orbit increases, the issue of flight safety in spacecraft formation orbit control has become increasingly prominent. With this in mind, this paper designs a second-order terminal sliding mode controller for spacecraft formation obstacle avoidance based on an [...] Read more.
As the number of satellites in orbit increases, the issue of flight safety in spacecraft formation orbit control has become increasingly prominent. With this in mind, this paper designs a second-order terminal sliding mode controller for spacecraft formation obstacle avoidance based on an artificial potential function (APF). To demonstrate the effectiveness of the controller, this paper first constructs a Lyapunov function to prove its stability and then verifies its theoretical validity through numerical simulation. Finally, a satellite simulator is used for semi-physical simulation to verify the practical effectiveness of the controller proposed in this paper. Full article
(This article belongs to the Special Issue Design, Dynamics and Control of Robots)
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10 pages, 5263 KiB  
Article
Low-Profile High-Efficiency Transmitarray Antenna for Beamforming Applications
by Jae-Gon Lee and Jeong-Hae Lee
Electronics 2023, 12(14), 3178; https://doi.org/10.3390/electronics12143178 - 21 Jul 2023
Cited by 2 | Viewed by 1261
Abstract
A low-profile high-efficiency transmitarray antenna (TA) for beamforming applications is proposed and investigated in this paper. The partial H-plane waveguide slot array antenna is employed as the compact low-profile feeding structure of the beamforming TA. The designed TA can achieve a high taper [...] Read more.
A low-profile high-efficiency transmitarray antenna (TA) for beamforming applications is proposed and investigated in this paper. The partial H-plane waveguide slot array antenna is employed as the compact low-profile feeding structure of the beamforming TA. The designed TA can achieve a high taper efficiency due to the multi-array sources and the compactness of the partial H-plane waveguide. Moreover, the proposed TA can inherently have a high spillover efficiency because the frequency selective surface (FSS) for beamforming is located just above the radiating slot. The FSS with a transmission phase variation of 2π is designed by a square patch array and used to manipulate the wave-front of the transmitted electromagnetic wave instead of a complicated feed network and phase shifters. To verify its beamforming characteristic, three types of FSSs to operate a forming angle of −40°, −20°, 0°, +20°, and +40° are designed at 12 GHz. The distance between the FSS and the slot array antenna is 0.1λ0, and the aperture efficiency is measured to be about 69%. The measured results, such as the reflection coefficient and the far-field radiation pattern, are in good agreement with the simulated results. From the measured results, the proposed TA is confirmed to have good beamforming characteristics and high aperture efficiency. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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22 pages, 669 KiB  
Article
Defense Mechanism to Generate IPS Rules from Honeypot Logs and Its Application to Log4Shell Attack and Its Variants
by Yudai Yamamoto and Shingo Yamaguchi
Electronics 2023, 12(14), 3177; https://doi.org/10.3390/electronics12143177 - 21 Jul 2023
Cited by 1 | Viewed by 1103
Abstract
The vulnerability of Apache Log4j, Log4Shell, is known for its widespread impact; many attacks that exploit Log4Shell use obfuscated attack patterns, and Log4Shell has revealed the importance of addressing such variants. However, there is no research which focuses on the response to variants. [...] Read more.
The vulnerability of Apache Log4j, Log4Shell, is known for its widespread impact; many attacks that exploit Log4Shell use obfuscated attack patterns, and Log4Shell has revealed the importance of addressing such variants. However, there is no research which focuses on the response to variants. In this paper, we propose a defense system that can protect against variants as well as known attacks. The proposed defense system can be divided into three parts: honeypots, machine learning, and rule generation. Honeypots are used to collect data, which can be used to obtain information about the latest attacks. In machine learning, the data collected by honeypots are used to determine whether it is an attack or not. It generates rules that can be applied to an IPS (Intrusion Prevention System) to block access that is determined to be an attack. To investigate the effectiveness of this system, an experiment was conducted using test data collected by honeypots, with the conventional method using Suricata, an IPS, as a comparison. Experimental results show that the discrimination performance of the proposed method against variant attacks is about 50% higher than that of the conventional method, indicating that the proposed method is an effective method against variant attacks. Full article
(This article belongs to the Special Issue Data Driven Security)
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21 pages, 9132 KiB  
Article
SP-YOLO-Lite: A Lightweight Violation Detection Algorithm Based on SP Attention Mechanism
by Zhihao Huang, Jiajun Wu, Lumei Su, Yitao Xie, Tianyou Li and Xinyu Huang
Electronics 2023, 12(14), 3176; https://doi.org/10.3390/electronics12143176 - 21 Jul 2023
Cited by 1 | Viewed by 1115
Abstract
In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However, common general-purpose object detection algorithms are [...] Read more.
In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However, common general-purpose object detection algorithms are difficult to deploy on low-computational-power embedded platforms situated at the edge due to their high model complexity. These algorithms suffer from drawbacks such as low operational efficiency, slow detection speed, and high energy consumption. To address this issue, a lightweight violation detection algorithm based on the SP (Segmentation-and-Product) attention mechanism, named SP-YOLO-Lite, is proposed to improve the YOLOv5s detection algorithm and achieve low-cost deployment and efficient operation of object detection algorithms on low-computational-power monitoring platforms. First, to address the issue of excessive complexity in backbone networks built with conventional convolutional modules, a Lightweight Convolutional Block was employed to construct the backbone network, significantly reducing computational and parameter costs while maintaining high detection model accuracy. Second, in response to the problem of existing attention mechanisms overlooking spatial local information, we introduced an image segmentation operation and proposed a novel attention mechanism called Segmentation-and-Product (SP) attention. It enables the model to effectively capture local informative features of the image, thereby enhancing model accuracy. Furthermore, a Neck network that is both lightweight and feature-rich is proposed by introducing Depthwise Separable Convolution and Segmentation-and-Product attention module to Path Aggregation Network, thus addressing the issue of high computation and parameter volume in the Neck network of YOLOv5s. Experimental results show that compared with the baseline network YOLOv5s, the proposed SP-YOLO-Lite model reduces the computation and parameter volume by approximately 70%, achieving similar detection accuracy on both the VOC dataset and our self-built SMPC dataset. Full article
(This article belongs to the Section Artificial Intelligence)
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22 pages, 7107 KiB  
Article
A Multi-Modal Modulation Recognition Method with SNR Segmentation Based on Time Domain Signals and Constellation Diagrams
by Ruifeng Duan, Xinze Li, Haiyan Zhang, Guoting Yang, Shurui Li, Peng Cheng and Yonghui Li
Electronics 2023, 12(14), 3175; https://doi.org/10.3390/electronics12143175 - 21 Jul 2023
Cited by 1 | Viewed by 1145
Abstract
Deep-learning-based automatic modulation recognition (AMR) has recently attracted significant interest due to its high recognition accuracy and the lack of a need to manually set classification standards. However, it is extremely challenging to achieve a high recognition accuracy in increasingly complex channel environments [...] Read more.
Deep-learning-based automatic modulation recognition (AMR) has recently attracted significant interest due to its high recognition accuracy and the lack of a need to manually set classification standards. However, it is extremely challenging to achieve a high recognition accuracy in increasingly complex channel environments and balance the complexity. To address this issue, we propose a multi-modal AMR neural network model with SNR segmentation called M-LSCANet, which integrates an SNR segmentation strategy, lightweight residual stacks, skip connections, and an attention mechanism. In the proposed model, we use time domain I/Q data and constellation diagram data only in medium and high signal-to-noise (SNR) regions to jointly extract the signal features. But for the low SNR region, only I/Q signals are used. This is because constellation diagrams are very recognizable in the medium and high SNRs, which is conducive to distinguishing high-order modulation. However, in the low SNR region, excessive similarity and the blurring of constellations caused by heavy noise will seriously interfere with modulation recognition, resulting in performance loss. Remarkably, the proposed method uses lightweight residuals stacks and rich ski connections, so that more initial information is retained to learn the constellation diagram feature information and extract the time domain features from shallow to deep, but with a moderate complexity. Additionally, after feature fusion, we adopt the convolution block attention module (CBAM) to reweigh both the channel and spatial domains, further improving the model’s ability to mine signal characteristics. As a result, the proposed approach significantly improves the overall recognition accuracy. The experimental results on the RadioML 2016.10B public dataset, with SNR ranging from −20 dB to 18 dB, show that the proposed M-LSCANet outperforms existing methods in terms of classification accuracy, achieving 93.4% and 95.8% at 0 dB and 12 dB, respectively, which are improvements of 2.7% and 2.0% compared to TMRN-GLU. Moreover, the proposed model exhibits a moderate parameter number compared to state-of-the-art methods. Full article
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