International Journal of Intelligent Networks最新文献_第3页

A new intelligent controller based on integral sliding mode control and extended state observer for nonlinear MIMO drone quadrotor 基于积分滑模控制和扩展状态观测器的非线性 MIMO 无人机四旋翼新型智能控制器

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.01.005

Moussa Abdillah , El Mehdi Mellouli , Touria Haidi

{"title":"A new intelligent controller based on integral sliding mode control and extended state observer for nonlinear MIMO drone quadrotor","authors":"Moussa Abdillah , El Mehdi Mellouli , Touria Haidi","doi":"10.1016/j.ijin.2024.01.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.01.005","url":null,"abstract":"<div><p>Unmanned aerial vehicles (UAVs) control faces major challenges such as dynamic complexity, unknown external disturbances, parametric uncertainties, time-varying states and delays. The literature proposes different techniques to address these challenges, but little attention has been paid to the design of a hybrid controller combining the advantages of these techniques to improve system performance. This research therefore aims to investigate the design of such a hybrid controller. In this paper, we present a novel intelligent controller based on Integral Sliding Mode Control (ISMC) and Extended State Observer (ESO) for a nonlinear Multiple Input Multiple Output (MIMO) drone quadrotor. First, the kinematic and dynamic models of our quadrotor drone are presented. Second, the ESO is used to estimate external disturbances and model uncertainties. Third, to overcome the problem of the reaching phase and the steady-state error, a new nonlinear ISMC is designed. The additive term of the ISMC structure has also overcome the problem of external disturbances and modelling errors, as well as observational errors. Fourth, an Adaptive Neural Network (ANN) switching control law is developed to surmount the chattering phenomenon. In addition, the stability of the control system is verified using Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed control method are proved by simulation results. The results show that the proposed approach can handle external disturbances and eliminate chatter, leading to smooth control laws and lower power consumption, which is excellent from an energy efficiency perspective.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 49-62"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000058/pdfft?md5=bf10b8e94c36179eb2344a4711679e92&pid=1-s2.0-S2666603024000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative artificial intelligence for distributed learning to enhance smart grid communication 用于分布式学习的生成式人工智能增强智能电网通信

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.05.007

Seyed Mahmoud Sajjadi Mohammadabadi , Mahmoudreza Entezami , Aidin Karimi Moghaddam , Mansour Orangian , Shayan Nejadshamsi

{"title":"Generative artificial intelligence for distributed learning to enhance smart grid communication","authors":"Seyed Mahmoud Sajjadi Mohammadabadi , Mahmoudreza Entezami , Aidin Karimi Moghaddam , Mansour Orangian , Shayan Nejadshamsi","doi":"10.1016/j.ijin.2024.05.007","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.007","url":null,"abstract":"<div><p>Machine learning models are the backbone of smart grid optimization, but their effectiveness hinges on access to vast amounts of training data. However, smart grids face critical communication bottlenecks due to the ever-increasing volume of data from distributed sensors. This paper introduces a novel approach leveraging Generative Artificial Intelligence (GenAI), specifically a type of pre-trained Foundation Model (FM) architecture suitable for time series data due to its efficiency and privacy-preserving properties. These GenAI models are distributed to agents, or data holders, empowering them to fine-tune the foundation model with their local datasets. By fine-tuning the foundation model, the updated model can produce synthetic data that mirrors real-world grid conditions. The server aggregates fine-tuned model from all agents and then generates synthetic data which considers all data collected in the grid. This synthetic data can be used to train global machine learning models for specific tasks like anomaly detection and energy optimization. Then, the trained task models are distributed to agents in the grid to leverage them. The paper highlights the advantages of GenAI for smart grid communication, including reduced communication burden, enhanced privacy through anonymized data transmission, and improved efficiency and scalability. By enabling a distributed and intelligent communication architecture, GenAI introduces a novel way for a more secure, efficient, and sustainable energy future.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 267-274"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000265/pdfft?md5=b36de28bb4f3c1a5f7cec09e98576268&pid=1-s2.0-S2666603024000265-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM 增强不平衡在线评论中的方面类别检测：使用 Select-SMOTE 和 LightGBM 的综合方法

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.10.002

Chuanjun Zhao , Zhihe Yan , Xuzhuang Sun , Meiling Wu

{"title":"Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM","authors":"Chuanjun Zhao , Zhihe Yan , Xuzhuang Sun , Meiling Wu","doi":"10.1016/j.ijin.2024.10.002","DOIUrl":"10.1016/j.ijin.2024.10.002","url":null,"abstract":"<div><div>Aspect category detection (ACD) is a pivotal subtask within the field of sentiment analysis in natural language processing, aiming to identify implicit aspect category information in online review texts. In real-world scenarios of online review category detection tasks, data imbalance often arises, leading to skewed distributions among distinct review categories. This phenomenon poses substantial challenges for accurately recognizing minority categories through modeling. To address this, we propose a method for detecting imbalanced aspect categories by combining the selective synthetic over-sampling (Select-SMOTE) algorithm with the light gradient boosting machine (LightGBM). Our approach commences with text data representation through features, followed by a strategy involving joint sample partitioning and boundary optimization within the feature space to generate minority class samples. This partitioning strategy aligns generated data more closely with the original distribution, while the boundary optimization module enhances classification performance by eliminating samples near boundaries. Subsequently, the balanced dataset is input to the LGB model, enabling the extraction of aspect category information through parameter optimization and class weight assignment. Finally, our method is evaluated using the SemEval and SentiHood datasets and compared with prevailing sampling methods and classification models. Empirical results manifestly demonstrate the method’s superiority across diverse metrics, reflecting robustness and effective mitigation of imbalanced data challenges in ACD.</div></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 364-372"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face recognition technology for video surveillance integrated with particle swarm optimization algorithm 集成粒子群优化算法的视频监控人脸识别技术

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.02.008

You Qian

{"title":"Face recognition technology for video surveillance integrated with particle swarm optimization algorithm","authors":"You Qian","doi":"10.1016/j.ijin.2024.02.008","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.02.008","url":null,"abstract":"<div><p>With the rapid development of video surveillance technology, face recognition has become an important security and surveillance tool. To improve the accuracy and applicability of face recognition in video surveillance, this study improved the Inertia Weight (IW) and Learning Factor (LF) based on the Particle Swarm Optimization (PSO) algorithm. Support Vector Machine (SVM) algorithm and Local Binary Mode (LBP) were used to optimize the processing. The results showed that the optimal solution could be obtained after 10 iterations, and the recognition accuracy reached 92.3%. When the number of iterations reached 40, the recognition accuracy inertia weight reached 99.7%. The average operating time of the original PSO algorithm and the optimized PSO algorithm was 26.3 s and 24.7 s, respectively. This shows that the optimization algorithm not only improves the recognition accuracy, but also shortens the operation time, and enhances the convergence performance and robustness to varying degrees. The improved model can improve the recognition rate of video surveillance system, indicating that the optimization algorithm has great application potential in the video surveillance face recognition.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 145-153"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000149/pdfft?md5=3d3263b33fe3d1c605dd0e3b65dc3425&pid=1-s2.0-S2666603024000149-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NVS-GAN: Benefit of generative adversarial network on novel view synthesis NVS-GAN：生成式对抗网络对新型视图合成的益处

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.04.002

H.S. Shrisha , V. Anupama

{"title":"NVS-GAN: Benefit of generative adversarial network on novel view synthesis","authors":"H.S. Shrisha , V. Anupama","doi":"10.1016/j.ijin.2024.04.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.04.002","url":null,"abstract":"<div><p>The methodology to generate new views for an object from provided input object view is called Novel View Synthesis (NVS). Humans imagine novel views through prior knowledge gathered through their lifetime. NVS-GAN predicts the novel views through computation. Literature survey reveals that there are limited NVS models with low Trainable Parameter Count (TPC) and low model size. Also, a study on the effect of different loss functions on NVS models was lacking. Lowering the TPC indicates less computational steps for the model to predict the output, therefore desirable. Combined with a low model size, the proposed model will become more suitable for deployment in diverse devices having limited resources for computation. Application of right combination of loss functions yield better accuracy. To address these research gaps, NVS-GAN is proposed. NVS-GAN is a Generative Adversarial Network (GAN) approach which yields NVS-Generator which performs NVS. NVS-Generator incorporates identity skip connections, bilinear sampling module, Depthwise Separable Convolution (DSC) as design features and results in low TPC, model size. In addition to discriminator loss, NVS-GAN is trained with different combinations of loss functions i.e. Mean Absolute Error (MAE) loss, Structural Similarity Index Measure (SSIM) loss, Huber loss on chair and car objects of ShapeNet dataset. The performance of NVS-Generator on test set measured in terms of MAE and SSIM is tabulated and analysed. The performance is compared with existing NVS models. The proposed NVS-GAN experiment recorded reduction in NVS-Generator TPC in 37 %–54.6 % range and reduction in model size between 37.2 % and 47.6 % range. NVS-Generator reduced MAE upto 55 % and improved SSIM upto 4 % than existing models. Summarily, NVS-GAN increased model performance and made the model “lightweight”.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 184-195"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000186/pdfft?md5=1c1cfb2444eb7781ad1ce312521adfae&pid=1-s2.0-S2666603024000186-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variable rate power-controlled batch-based channel assignment for enhanced throughput in cognitive radio networks 基于可变速率功率控制的批量信道分配，提高认知无线电网络的吞吐量

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.04.001

Ahmed Musa , Haythem Bany Salameh , Rami Halloush , Renad Bataineh , Mahmoud M. Qasaymeh

{"title":"Variable rate power-controlled batch-based channel assignment for enhanced throughput in cognitive radio networks","authors":"Ahmed Musa , Haythem Bany Salameh , Rami Halloush , Renad Bataineh , Mahmoud M. Qasaymeh","doi":"10.1016/j.ijin.2024.04.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.04.001","url":null,"abstract":"<div><p>The number of users in wireless networks, such as mobile and Internet-of-Things networks, is witnessing a tremendous increase, turning the available frequency spectrum into a scarce resource that needs to be efficiently utilized. Cognitive radio (CR) is a key technology for achieving spectrum efficiency by continuously sensing and detecting frequency bands that are not used by licensed primary users (PU) and allowing unlicensed secondary users (SUs) to use them. One of the main challenges in CR is the design of a medium access control (MAC) protocol that ensures efficient spectrum sharing by SUs without disrupting the connectivity of PUs. To achieve that, many of the existing MAC protocols in the literature allow multiple SU transmissions to proceed simultaneously by performing batch-based power control decisions to limit mutual interference between them. Interestingly, the majority of such protocols are demand-rate unaware; i.e., all SUs are granted the same data rate, regardless of their data rate demand. In this paper, we highlight the severe drawbacks of demand-rate unawareness and propose the rate-aware power-controlled channel assignment (RPCCA) MAC protocol, which performs batch-based simultaneous channel assignment decisions to competing SUs along with power control to limit mutual interference, while taking into account the variable demand-rate across SUs. Simulation experiments have demonstrated that the RPCCA protocol offers substantial performance improvements over existing demand-rate unaware CR-based MAC protocols.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 175-183"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000174/pdfft?md5=838c656859212eb1731f3eca27b3befe&pid=1-s2.0-S2666603024000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revolutionizing prostate cancer diagnosis: Unleashing the potential of an optimized deep belief network for accurate Gleason grading in histological images 彻底改变前列腺癌诊断：释放优化深度信念网络的潜力，在组织学图像中准确进行格里森分级

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.05.004

S. Angel Latha Mary , S. Siva Subramanian , G. Priyanka , T. Vijayakumar , Suganthi Alagumalai

{"title":"Revolutionizing prostate cancer diagnosis: Unleashing the potential of an optimized deep belief network for accurate Gleason grading in histological images","authors":"S. Angel Latha Mary , S. Siva Subramanian , G. Priyanka , T. Vijayakumar , Suganthi Alagumalai","doi":"10.1016/j.ijin.2024.05.004","DOIUrl":"10.1016/j.ijin.2024.05.004","url":null,"abstract":"<div><p>PC (Prostate Cancer) is the second highest cause of death due to cancer in men globally. Proper detection and treatment are critical for halting or controlling the growth and spread of cancer cells within the human organism. However, evaluating these sorts of images is difficult and time-consuming, requiring histopathological image recognition as the most reliable method for treating PC because of its distinct visual characteristics. Risk evaluation and treatment planning rely heavily on histological image-based Gleason grading of prostate tumors. This work introduces an innovative approach to histological image analysis for prostate cancer diagnosis and Gleason grading. The Elephant Herding Optimization-based Hyper-parameter Convolutional Deep Belief Network (CDBN-EHO) is presented alongside a grading network head-optimized deep belief network technique for multi-task prediction. Leveraging an effective Bayesian inference method, fully linked Conditional Random Field (CRF) techniques are utilized for segmentation, with pairwise boundary capacities determined by a linear mixture of Gaussian kernels. The multi-task approach aims to enhance performance by incorporating contextual information, leading to breakthrough results in the identification of epithelial cells and the grading of Gleason scores. The objective of this study is to demonstrate the effectiveness of the optimized deep belief network technique in improving diagnostic accuracy and efficiency for prostate cancer diagnosis and Gleason grading in histological images.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 241-254"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266660302400023X/pdfft?md5=eff597db63cf46fb94cc2cd30ac2bade&pid=1-s2.0-S266660302400023X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141145505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security experimental framework of trajectory planning for autonomous vehicles 自动驾驶汽车轨迹规划安全实验框架

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.08.003

Sujoud Al-sheyab , Zakarea Al-shara , Osama Al-khaleel

{"title":"Security experimental framework of trajectory planning for autonomous vehicles","authors":"Sujoud Al-sheyab , Zakarea Al-shara , Osama Al-khaleel","doi":"10.1016/j.ijin.2024.08.003","DOIUrl":"10.1016/j.ijin.2024.08.003","url":null,"abstract":"<div><p>In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a critical concern. The potential for attacks within AV networks, exemplified by the Trajectory Privacy Attack on Autonomous Driving (T-PAAD), underscores the urgency for robust security measures. Unfortunately, existing simulations for preemptively assessing the T-PAAD attack's impact are scarce. This paper introduces the Security Experimental Framework for Autonomous Vehicles (SEFAV), designed to address this gap by providing a versatile platform for simulating security scenarios in AV environments. SEFAV is cross-platform and compatible with different operating systems such as Windows and Linux, enhancing accessibility for researchers and practitioners. Our primary focus lies in showcasing the T-PAAD attack within our framework, highlighting its efficacy in evaluating and fortifying AV security.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 315-324"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000307/pdfft?md5=892f01ae9891afc0fe2026f438b5a155&pid=1-s2.0-S2666603024000307-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks 面向基于无人机的智能室内网络的通用混合 LiFi-WiFi UniPHY 学习框架

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.05.008

Rizwana Ahmad , Dil Nashin Anwar , Haythem Bany Salameh , Hany Elgala , Moussa Ayyash , Sufyan Almajali , Reyad El-Khazali

{"title":"Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks","authors":"Rizwana Ahmad , Dil Nashin Anwar , Haythem Bany Salameh , Hany Elgala , Moussa Ayyash , Sufyan Almajali , Reyad El-Khazali","doi":"10.1016/j.ijin.2024.05.008","DOIUrl":"https://doi.org/10.1016/j.ijin.2024.05.008","url":null,"abstract":"<div><p>Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 255-266"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000277/pdfft?md5=15fe93a13ef7559169ab6920f8be3686&pid=1-s2.0-S2666603024000277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141241222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A method of vehicle networking environment information sharing based on distributed fountain code 基于分布式喷泉代码的车联网环境信息共享方法

International Journal of Intelligent Networks Pub Date : 2024-01-01 DOI: 10.1016/j.ijin.2024.01.001

Jianhang Liu , Xinyao Wang , Haibin Zhai , Shibao Li , Xuerong Cui , Qian Zhang

{"title":"A method of vehicle networking environment information sharing based on distributed fountain code","authors":"Jianhang Liu , Xinyao Wang , Haibin Zhai , Shibao Li , Xuerong Cui , Qian Zhang","doi":"10.1016/j.ijin.2024.01.001","DOIUrl":"10.1016/j.ijin.2024.01.001","url":null,"abstract":"<div><p>The exchange of perceptual information between autonomous vehicles could significantly improve driving safety. In general, obtaining more information means driving more safely. However, Frequent information sharing consumes a significant amount of channel bandwidth resources, which will reduce transmission efficiency and increase delay, especially in crowded cities. This paper presents a novel method of motion prediction compensation to solve this problem. Firstly, we propose a distributed fountain coding scheme to improve transmission efficiency and reduce vehicles’ delay in acquiring peripheral information. Secondly, we design a mobile prediction model and information transmission control algorithm to reduce traffic while ensuring information reliability. The simulation results show that the prediction accuracy of this method is above 94 %, the information transmission is reduced by more than 50 %, and the vehicle perception rate is increased by 34 %.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"5 ","pages":"Pages 19-29"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603024000010/pdfft?md5=34d2d987154140687cc34de68df3a69b&pid=1-s2.0-S2666603024000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139538922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0