Journal of The Franklin Institute-engineering and Applied Mathematics最新文献_第2页

Distributed adaptive event-triggered finite-time fault-tolerant containment control for multi-UAVs with input constraints and actuator failures 针对具有输入约束和执行器故障的多无人飞行器的分布式自适应事件触发有限时间容错遏制控制

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-29 DOI: 10.1016/j.jfranklin.2024.107308

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引用次数: 0

Extended state observer-based finite-time trajectory tracking control for wheeled mobile robots under FDI attacks FDI 攻击下基于扩展状态观测器的轮式移动机器人有限时间轨迹跟踪控制

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-28 DOI: 10.1016/j.jfranklin.2024.107304

引用次数: 0

A zonotope-based fault detection method for switched systems with parameter uncertainties and multiple time delays 针对参数不确定和多时间延迟的开关系统的基于区域顶的故障检测方法

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-28 DOI: 10.1016/j.jfranklin.2024.107305

引用次数: 0

MIMO Super-Twisting Controller using a passivity-based design 采用基于被动性设计的 MIMO 超扭曲控制器

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-28 DOI: 10.1016/j.jfranklin.2024.107296

引用次数: 0

Consensus formation control of wheeled mobile robots with mixed disturbances under input constraints 输入约束条件下具有混合干扰的轮式移动机器人的共识形成控制

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-27 DOI: 10.1016/j.jfranklin.2024.107300

{"title":"Consensus formation control of wheeled mobile robots with mixed disturbances under input constraints","authors":"","doi":"10.1016/j.jfranklin.2024.107300","DOIUrl":"10.1016/j.jfranklin.2024.107300","url":null,"abstract":"<div><div>This paper addresses the problem of distributed consensus-based formation control for wheeled mobile robots (WMRs) under the influence of mixed disturbances, including both random noise and non-random disturbances. A consensus formation auxiliary subsystem is constructed based on the leader’s position estimated by the distributed estimator. A formation tracking subsystem for each robot is constructed based on the trajectory tracking error method. The above two subsystems are constructed into an extended formation modeling system. Further, a distributed model predictive control (DMPC) is designed to control this system without disturbance, and the controller is solved by means of a general-purpose neural network. A combination of Kalman filter (KF) and extended state observer (ESO) is intended to reduce the effect of both non-random disturbances and random noise, hence increasing the controller’s resilience to disturbances. Moreover, a composite control law is designed to ensure the controller’s effectiveness. Finally, simulation results demonstrate that the proposed control strategy is well-suited to addressing the problem, as it not only achieves accurate formation control but also effectively regulates the robot’s physical constraints while suppressing both non-random disturbances and random noise.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy adaptive fixed-time control for output-constrained uncertain nonstrict-feedback time-delay systems 输出受限不确定非严格反馈时延系统的模糊自适应固定时间控制

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-27 DOI: 10.1016/j.jfranklin.2024.107302

{"title":"Fuzzy adaptive fixed-time control for output-constrained uncertain nonstrict-feedback time-delay systems","authors":"","doi":"10.1016/j.jfranklin.2024.107302","DOIUrl":"10.1016/j.jfranklin.2024.107302","url":null,"abstract":"<div><div>This paper deals with the problem of fuzzy adaptive fixed-time control for a class of uncertain nonlinear nonstrict feedback systems with time delays and output constraints. The negative effects of time delays on system performance and stability are overcome by introducing a new Barrier Lyapunov–Krasovskii (BLK) function and an innovative inequality. In addition, the singularity problem that arises in the process of deriving the control and adaptive laws, is solved by the proposed BLK function. The controller is designed in the backstepping framework which ensures that the system output does not violate predefined constraints. The proposed controller needs less information from the system and therefore has less implementation complexity than previous references, which leads to a more straightforward implementation. A fuzzy system approximation is utilized to overcome the uncertainties of the model. It is guaranteed that all closed-loop system signals converge to small neighborhoods around zero in a fixed time. Finally, an example is presented to verify that the proposed method achieves the control objectives.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face recognition method based on fusion of improved MobileFaceNet and adaptive Gamma algorithm 基于改进型 MobileFaceNet 和自适应伽马算法融合的人脸识别方法

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-27 DOI: 10.1016/j.jfranklin.2024.107306

{"title":"Face recognition method based on fusion of improved MobileFaceNet and adaptive Gamma algorithm","authors":"","doi":"10.1016/j.jfranklin.2024.107306","DOIUrl":"10.1016/j.jfranklin.2024.107306","url":null,"abstract":"<div><div>MobileFaceNet face recognition algorithm is a relatively mainstream face recognition algorithm at present. Its advantages of small memory and fast running speed make it widely used in embedded devices. Due to the limited face image acquisition capability of embedded devices, the accuracy of face recognition is often reduced due to uneven illumination and poor exposure quality. In order to solve this problem, a face recognition algorithm based on the fusion of MobileFaceNet and adaptive Gamma algorithm is proposed. The application of the algorithm proposed in this paper in image preprocessing is as follows. Firstly, adaptive Gamma correction is used to improve the brightness of the face image. Then, the edge of the face image is enhanced by the Laplace operator. Finally, a linear weighted fusion was performed between the Gamma corrected image and the enhanced edge image to obtain the pre-processed face image. At the same time, we have improved the traditional MobileFaceNet network. The feature extraction network MobileFaceNet has been improved by adding a Stylebased Recall Module (SRM) attention mechanism to its bottom neck layer, utilizing the mean and standard deviation of input features to improve the ability to capture global information and enhance more important feature information. Finally, the proposed method was verified on the LFW and Agedb face test set. The experimental results show that the adaptive Gamma algorithm proposed in this paper and the improvement of MobileFaceNet can achieve a face recognition accuracy of 99.27 % on LFW dataset and 90.18 % on Agedb dataset while only increasing the model size by 0.4 M and the processing speed for each image is enhanced by 4 ms. which can effectively improve the accuracy of face recognition and better application prospects on embedded devices. The method presented in this article has certain practical significance.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two improved generalized extended stochastic gradient algorithms for CARARMA systems CARARMA 系统的两种改进型广义扩展随机梯度算法

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107295

引用次数: 0

Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting 用于交通流量预测的时空相似性融合图对抗卷积网络

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107299

{"title":"Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting","authors":"","doi":"10.1016/j.jfranklin.2024.107299","DOIUrl":"10.1016/j.jfranklin.2024.107299","url":null,"abstract":"<div><div>Traffic flow forecasting is integral to the advancement of intelligent transportation systems and the development of smart cities. This paper introduces a novel model, the Spatial–Temporal Similarity Fusion Graphs Adversarial Convolutional Networks (STSF-GACN), which leverages advanced data preprocessing techniques to enhance the predictive accuracy and efficiency of traffic flow forecasting.</div><div>The innovation of our approach lies in the meticulous construction of the spatial–temporal similarity matrix through the precise calculation of temporal and spatial similarities. This matrix forms the backbone of our model, serving as the generator in the integrated Generative Adversarial Network (GAN) architecture. The Spatial–Temporal Similarity Fusion Adaptive Graph Convolutional Network, developed as part of our GAN’s generator, utilizes cutting-edge techniques such as the Wasserstein distance and Dynamic Time Warping to optimize the adaptive adjacency matrix, enabling the model to capture latent spatial–temporal correlations with unprecedented depth and precision.</div><div>The discriminator of the GAN further refines the model by evaluating the accuracy of the traffic predictions, ensuring that the generative model produces results that are not only accurate but also robust against varying traffic conditions. This cohesive integration of GAN into the model architecture allows for a significant improvement in prediction accuracy and convergence speed, moving beyond traditional forecasting methods.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lyapunov–Razumikhin control strategy for stochastic nonlinear delayed systems with polynomial conditions 具有多项式条件的随机非线性延迟系统的 Lyapunov-Razumikhin 控制策略

IF 3.7 3区计算机科学

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107288

引用次数: 0