Electronics最新文献_第2页

Specific Emitter Identification Algorithm Based on Time–Frequency Sequence Multimodal Feature Fusion Network 基于时频序列多模态特征融合网络的特定发射器识别算法

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-18 DOI: 10.3390/electronics13183703

Yuxuan He, Kunda Wang, Qicheng Song, Huixin Li, Bozhi Zhang

{"title":"Specific Emitter Identification Algorithm Based on Time–Frequency Sequence Multimodal Feature Fusion Network","authors":"Yuxuan He, Kunda Wang, Qicheng Song, Huixin Li, Bozhi Zhang","doi":"10.3390/electronics13183703","DOIUrl":"https://doi.org/10.3390/electronics13183703","url":null,"abstract":"Specific emitter identification is a challenge in the field of radar signal processing. Its aims to extract individual fingerprint features of the signal. However, early works are all designed using either signal or time–frequency image and heavily rely on the calculation of hand-crafted features or complex interactions in high-dimensional feature space. This paper introduces the time–frequency multimodal feature fusion network, a novel architecture based on multimodal feature interaction. Specifically, we designed a time–frequency signal feature encoding module, a wvd image feature encoding module, and a multimodal feature fusion module. Additionally, we propose a feature point filtering mechanism named FMM for signal embedding. Our algorithm demonstrates high performance in comparison with the state-of-the-art mainstream identification methods. The results indicate that our algorithm outperforms others, achieving the highest accuracy, precision, recall, and F1-score, surpassing the second-best by 9.3%, 8.2%, 9.2%, and 9%. Notably, the visual results show that the proposed method aligns with the signal generation mechanism, effectively capturing the distinctive fingerprint features of radar data. This paper establishes a foundational architecture for the subsequent multimodal research in SEI tasks.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal Social Media Fake News Detection Based on 1D-CCNet Attention Mechanism 基于 1D-CCNet 注意力机制的多模态社交媒体假新闻检测

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-18 DOI: 10.3390/electronics13183700

Yuhan Yan, Haiyan Fu, Fan Wu

{"title":"Multimodal Social Media Fake News Detection Based on 1D-CCNet Attention Mechanism","authors":"Yuhan Yan, Haiyan Fu, Fan Wu","doi":"10.3390/electronics13183700","DOIUrl":"https://doi.org/10.3390/electronics13183700","url":null,"abstract":"Due to the explosive rise of multimodal content in online social communities, cross-modal learning is crucial for accurate fake news detection. However, current multimodal fake news detection techniques face challenges in extracting features from multiple modalities and fusing cross-modal information, failing to fully exploit the correlations and complementarities between different modalities. To address these issues, this paper proposes a fake news detection model based on a one-dimensional CCNet (1D-CCNet) attention mechanism, named BTCM. This method first utilizes BERT and BLIP-2 encoders to extract text and image features. Then, it employs the proposed 1D-CCNet attention mechanism module to process the input text and image sequences, enhancing the important aspects of the bimodal features. Meanwhile, this paper uses the pre-trained BLIP-2 model for object detection in images, generating image descriptions and augmenting text data to enhance the dataset. This operation aims to further strengthen the correlations between different modalities. Finally, this paper proposes a heterogeneous cross-feature fusion method (HCFFM) to integrate image and text features. Comparative experiments were conducted on three public datasets: Twitter, Weibo, and Gossipcop. The results show that the proposed model achieved excellent performance.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Overtaking Path Planning and Trajectory Tracking Control Based on Critical Safety Distance 基于临界安全距离的自动超车路径规划和轨迹跟踪控制

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-18 DOI: 10.3390/electronics13183698

Juan Huang, Songlin Sun, Kai Long, Lairong Yin, Zhiyong Zhang

{"title":"Automatic Overtaking Path Planning and Trajectory Tracking Control Based on Critical Safety Distance","authors":"Juan Huang, Songlin Sun, Kai Long, Lairong Yin, Zhiyong Zhang","doi":"10.3390/electronics13183698","DOIUrl":"https://doi.org/10.3390/electronics13183698","url":null,"abstract":"The overtaking process for autonomous vehicles must prioritize both efficiency and safety, with safe distance being a crucial parameter. To address this, we propose an automatic overtaking path planning method based on minimal safe distance, ensuring both maneuvering efficiency and safety. This method combines the steady movement and comfort of the constant velocity offset model with the smoothness of the sine function model, creating a mixed-function model that is effective for planning lateral motion. For precise longitudinal motion planning, the overtaking process is divided into five stages, with each stage’s velocity and travel time calculated. To enhance the control system, the model predictive control (MPC) algorithm is applied, establishing a robust trajectory tracking control system for overtaking. Numerical simulation results demonstrate that the proposed overtaking path planning method can generate smooth and continuous paths. Under the MPC framework, the autonomous vehicle efficiently and safely performs automatic overtaking maneuvers, showcasing the method’s potential to improve the performance and reliability of autonomous driving systems.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Multi-Feature Attention Network for Image Dehazing 用于图像去重的自适应多特征注意网络

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-18 DOI: 10.3390/electronics13183706

Hongyuan Jing, Jiaxing Chen, Chenyang Zhang, Shuang Wei, Aidong Chen, Mengmeng Zhang

{"title":"Adaptive Multi-Feature Attention Network for Image Dehazing","authors":"Hongyuan Jing, Jiaxing Chen, Chenyang Zhang, Shuang Wei, Aidong Chen, Mengmeng Zhang","doi":"10.3390/electronics13183706","DOIUrl":"https://doi.org/10.3390/electronics13183706","url":null,"abstract":"Currently, deep-learning-based image dehazing methods occupy a dominant position in image dehazing applications. Although many complicated dehazing models have achieved competitive dehazing performance, effective methods for extracting useful features are still under-researched. Thus, an adaptive multi-feature attention network (AMFAN) consisting of the point-weighted attention (PWA) mechanism and the multi-layer feature fusion (AMLFF) is presented in this paper. We start by enhancing pixel-level attention for each feature map. Specifically, we design a PWA block, which aggregates global and local information of the feature map. We also employ PWA to make the model adaptively focus on significant channels/regions. Then, we design a feature fusion block (FFB), which can accomplish feature-level fusion by exploiting a PWA block. The FFB and PWA constitute our AMLFF. We design an AMLFF, which can integrate three different levels of feature maps to effectively balance the weights of the inputs to the encoder and decoder. We also utilize the contrastive loss function to train the dehazing network so that the recovered image is far from the negative sample and close to the positive sample. Experimental results on both synthetic and real-world images demonstrate that this dehazing approach surpasses numerous other advanced techniques, both visually and quantitatively, showcasing its superiority in image dehazing.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Semantic Segmentation Algorithm for Street Scenes Based on Attention Mechanism and Feature Fusion 基于注意机制和特征融合的街景实时语义分割算法

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-18 DOI: 10.3390/electronics13183699

Bao Wu, Xingzhong Xiong, Yong Wang

{"title":"Real-Time Semantic Segmentation Algorithm for Street Scenes Based on Attention Mechanism and Feature Fusion","authors":"Bao Wu, Xingzhong Xiong, Yong Wang","doi":"10.3390/electronics13183699","DOIUrl":"https://doi.org/10.3390/electronics13183699","url":null,"abstract":"In computer vision, the task of semantic segmentation is crucial for applications such as autonomous driving and intelligent surveillance. However, achieving a balance between real-time performance and segmentation accuracy remains a significant challenge. Although Fast-SCNN is favored for its efficiency and low computational complexity, it still faces difficulties when handling complex street scene images. To address this issue, this paper presents an improved Fast-SCNN, aiming to enhance the accuracy and efficiency of semantic segmentation by incorporating a novel attention mechanism and an enhanced feature extraction module. Firstly, the integrated SimAM (Simple, Parameter-Free Attention Module) increases the network’s sensitivity to critical regions of the image and effectively adjusts the feature space weights across channels. Additionally, the refined pyramid pooling module in the global feature extraction module captures a broader range of contextual information through refined pooling levels. During the feature fusion stage, the introduction of an enhanced DAB (Depthwise Asymmetric Bottleneck) block and SE (Squeeze-and-Excitation) attention optimizes the network’s ability to process multi-scale information. Furthermore, the classifier module is extended by incorporating deeper convolutions and more complex convolutional structures, leading to a further improvement in model performance. These enhancements significantly improve the model’s ability to capture details and overall segmentation performance. Experimental results demonstrate that the proposed method excels in processing complex street scene images, achieving a mean Intersection over Union (mIoU) of 71.7% and 69.4% on the Cityscapes and CamVid datasets, respectively, while maintaining inference speeds of 81.4 fps and 113.6 fps. These results indicate that the proposed model effectively improves segmentation quality in complex street scenes while ensuring real-time processing capabilities.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault Prediction in Resistance Spot Welding: A Comparison of Machine Learning Approaches 电阻点焊中的故障预测：机器学习方法比较

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-18 DOI: 10.3390/electronics13183693

Gabriele Ciravegna, Franco Galante, Danilo Giordano, Tania Cerquitelli, Marco Mellia

{"title":"Fault Prediction in Resistance Spot Welding: A Comparison of Machine Learning Approaches","authors":"Gabriele Ciravegna, Franco Galante, Danilo Giordano, Tania Cerquitelli, Marco Mellia","doi":"10.3390/electronics13183693","DOIUrl":"https://doi.org/10.3390/electronics13183693","url":null,"abstract":"Resistance spot welding is widely adopted in manufacturing and is characterized by high reliability and simple automation in the production line. The detection of defective welds is a difficult task that requires either destructive or expensive and slow non-destructive testing (e.g., ultrasound). The robots performing the welding automatically collect contextual and process-specific data. In this paper, we test whether these data can be used to predict defective welds. To do so, we use a dataset collected in a real industrial plant that describes welding-related data labeled with ultrasonic quality checks. We use these data to develop several pipelines based on shallow and deep learning machine learning algorithms and test the performance of these pipelines in predicting defective welds. Our results show that, despite the development of different pipelines and complex models, the machine-learning-based defect detection algorithms achieve limited performance. Using a qualitative analysis of model predictions, we show that correct predictions are often a consequence of inherent biases and intrinsic limitations in the data. We therefore conclude that the automatically collected data have limitations that hamper fault detection in a running production plant.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconfigurable Intelligent Surface-Based Backscatter Communication for Data transmission 用于数据传输的可重构智能表面反向散射通信技术

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-18 DOI: 10.3390/electronics13183702

Xingquan Li, Hongxia Zheng, Chunlong He, Yong Wang, Guoqing Wang

引用次数: 0

Denoising Diffusion Implicit Model for Camouflaged Object Detection 用于伪装物体检测的去噪扩散隐含模型

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-17 DOI: 10.3390/electronics13183690

Wei Cai, Weijie Gao, Xinhao Jiang, Xin Wang, Xingyu Di

{"title":"Denoising Diffusion Implicit Model for Camouflaged Object Detection","authors":"Wei Cai, Weijie Gao, Xinhao Jiang, Xin Wang, Xingyu Di","doi":"10.3390/electronics13183690","DOIUrl":"https://doi.org/10.3390/electronics13183690","url":null,"abstract":"Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Bottom-Up Towards a Completely Decentralized Autonomous Electric Grid Based on the Concept of a Decentralized Autonomous Substation 基于分散式自主变电站概念，自下而上实现完全分散的自主电网

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-17 DOI: 10.3390/electronics13183683

Alain Aoun, Nadine Kashmar, Mehdi Adda, Hussein Ibrahim

{"title":"From Bottom-Up Towards a Completely Decentralized Autonomous Electric Grid Based on the Concept of a Decentralized Autonomous Substation","authors":"Alain Aoun, Nadine Kashmar, Mehdi Adda, Hussein Ibrahim","doi":"10.3390/electronics13183683","DOIUrl":"https://doi.org/10.3390/electronics13183683","url":null,"abstract":"The idea of a decentralized electric grid has shifted from being a concept to a reality. The growing integration of distributed energy resources (DERs) has transformed the traditional centralized electric grid into a decentralized one. However, while most efforts to manage and optimize this decentralization focus on the electrical infrastructure layer, the operational and control layer, as well as the data management layer, have received less attention. Current electric grids rely on centralized control centers (CCCs) that serve as the electric grid’s brain, where operators monitor, control, and manage the entire grid infrastructure. Hence, any disruption caused by a cyberattack or a natural event, disconnecting the CCC, could have numerous negative effects on grid operations, including socioeconomic impacts, equipment damage, market repercussions, and blackouts. This article introduces the idea of a fully decentralized electric grid that leverages autonomous smart substations and blockchain integration for decentralized data management and control. The aim is to propose a blockchain-enabled decentralized electric grid model and its potential impact on energy markets, sustainability, and resilience. The model presented underlines the transformative potential of decentralized autonomous grids in revolutionizing energy systems for better operability, management, and flexibility.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensor-Based Real-Time Monitoring Approach for Multi-Participant Workout Intensity Management 基于传感器的多人锻炼强度管理实时监测方法

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-17 DOI: 10.3390/electronics13183687

José Saias, Jorge Bravo

{"title":"Sensor-Based Real-Time Monitoring Approach for Multi-Participant Workout Intensity Management","authors":"José Saias, Jorge Bravo","doi":"10.3390/electronics13183687","DOIUrl":"https://doi.org/10.3390/electronics13183687","url":null,"abstract":"One of the significant advantages of technological evolution is the greater ease of collecting and analyzing data. Miniaturization, wireless communication protocols and IoT allow the use of sensors to collect data, with all the potential to support decision making in real time. In this paper, we describe the design and implementation of a digital solution to guide the intensity of training or physical activity, based on heart rate wearable sensors applied to participants in group sessions. Our system, featuring a unified engine that simplifies sensor management and minimizes user disruption, has been proven effective for real-time monitoring. It includes custom alerts during variable-intensity workouts, and ensures data preservation for subsequent analysis by physiologists or clinicians. This solution has been used in sessions of up to six participants and sensors up to 12 m away from the gateway device. We describe some challenges and constraints we face in collecting data from multiple and possibly different sensors simultaneously via Bluetooth Low Energy, and the approaches we follow to overcome them. We conduct an in-depth questionnaire to identify potential obstacles and drivers for system acceptance. We also discuss some possibilities for extension and improvement of our system.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0