Journal of The Franklin Institute-engineering and Applied Mathematics最新文献

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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 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-27 DOI: 10.1016/j.jfranklin.2024.107306

Jingwei Li, Yipei Ding, Zhiyu Shao, Wei Jiang

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.

MobileFaceNet 人脸识别算法是目前比较主流的人脸识别算法。其内存小、运行速度快等优点使其在嵌入式设备中得到广泛应用。由于嵌入式设备的人脸图像采集能力有限，经常会出现光照不均匀、曝光质量差等问题，导致人脸识别的准确率降低。为了解决这一问题，本文提出了一种基于 MobileFaceNet 和自适应伽马算法融合的人脸识别算法。本文提出的算法在图像预处理中的应用如下。首先，使用自适应伽马校正来提高人脸图像的亮度。然后，利用拉普拉斯算子增强人脸图像的边缘。最后，在伽马校正图像和增强的边缘图像之间进行线性加权融合，得到预处理后的人脸图像。与此同时，我们还改进了传统的 MobileFaceNet 网络。我们改进了特征提取网络 MobileFaceNet，在其颈部底层增加了基于风格的召回模块（SRM）关注机制，利用输入特征的平均值和标准差来提高捕捉全局信息的能力，并增强更重要的特征信息。最后，在 LFW 和 Agedb 人脸测试集上对所提出的方法进行了验证。实验结果表明，本文提出的自适应伽马算法和对 MobileFaceNet 的改进，在模型大小仅增加 0.4 M，每幅图像处理速度提高 4 ms 的情况下，在 LFW 数据集上的人脸识别准确率达到 99.27%，在 Agedb 数据集上的人脸识别准确率达到 90.18%，能有效提高人脸识别的准确率，在嵌入式设备上有更好的应用前景。本文提出的方法具有一定的现实意义。

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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 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107295

Lingling Lv , Yulin Zhang , Quanzhen Huang , Yu Wu

The paper innovatively proposes two improved generalized extended stochastic gradient (GESG) algorithms for the controlled autoregressive autoregressive moving average (CARARMA) system with autoregressive moving average (ARMA) model noise. Firstly, we propose a latest estimation based weighted generalized extended stochastic gradient (LE-WGESG) algorithm, which introduces multiple momentary corrections in the traditional parameter estimation process. By carefully adjusting the weighting coefficients of the correction quantities at different moments, the algorithm has a rapid and greater efficient convergence property. More importantly, utilizing the theory of moving data window, this paper also proposes a multi-innovation based latest estimated weighted generalized extended stochastic gradient (MI-LE-WGESG) algorithm, which can better capture the interactions among multiple correction terms and further improve the predictive ability of the model.

本文针对具有自回归移动平均（ARMA）模型噪声的受控自回归自回归移动平均（CARARMA）系统，创新性地提出了两种改进的广义扩展随机梯度（GESG）算法。首先，我们提出了一种基于最新估计的加权广义扩展随机梯度（LE-WGESG）算法，该算法在传统参数估计过程中引入了多个时刻修正。通过仔细调整修正量在不同时刻的加权系数，该算法具有快速和更高效的收敛特性。更重要的是，本文还利用移动数据窗理论，提出了基于多创新的最新估计加权广义扩展随机梯度（MI-LE-WGESG）算法，该算法能更好地捕捉多个修正项之间的相互作用，进一步提高模型的预测能力。

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 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107299

Bin Wang, Zhendan Long, Jinfang Sheng, Qiang Zhong

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.

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.

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.

交通流量预测是智能交通系统和智慧城市发展不可或缺的一部分。本文介绍了一种新型模型--时空相似性融合图对抗卷积网络（STSF-GACN），它利用先进的数据预处理技术提高了交通流量预测的准确性和效率。该矩阵构成了我们模型的主干，是集成生成对抗网络（GAN）架构中的生成器。时空相似性融合自适应图卷积网络是作为 GAN 生成器的一部分而开发的，它利用瓦瑟斯坦距离和动态时间扭曲等尖端技术来优化自适应邻接矩阵，使模型能够以前所未有的深度和精度捕捉潜在的时空相关性。这种将 GAN 与模型架构紧密结合的方法大大提高了预测精度和收敛速度，超越了传统的预测方法。

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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 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-26 DOI: 10.1016/j.jfranklin.2024.107288

Han-Wen Zhang , Zhen-Guo Liu , Wei-Hai Zhang

Numerous control schemes for stochastic systems involving time-varying delays need a strict slow time-delay condition, which does hold for some practical models. Also, some stochastic systems will have different structures as working conditions change. In this article, we will investigate the control of stochastic systems which contain time-varying delays and polynomial conditions. By providing the dynamic-gain based homogeneous domination approach, and selecting a new Lyapunov–Razumikhin (L–R) function, a universal dynamic controller for systems with different growing conditions is presented, and the slow time-delay condition is removed successfully. The algorithm is finally verified with a practical example.

涉及时变延迟的随机系统的许多控制方案都需要严格的慢速时变延迟条件，这在某些实际模型中确实成立。此外，一些随机系统会随着工作条件的变化而产生不同的结构。本文将研究包含时变延迟和多项式条件的随机系统的控制问题。通过提供基于动态增益的同质支配方法，并选择一种新的 Lyapunov-Razumikhin (L-R) 函数，提出了一种适用于不同生长条件系统的通用动态控制器，并成功地消除了慢时延条件。最后通过一个实际例子对该算法进行了验证。

引用次数: 0

Reachable set control of singular semi-Markov jump multi-agent systems with input delay via state decomposition method 通过状态分解法实现具有输入延迟的奇异半马尔可夫跃迁多代理系统的可达集控制

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-25 DOI: 10.1016/j.jfranklin.2024.107294

Mingde Liu , Liang Zhang , Ning Zhao , Sanaa Sharaf , Lun Li

This paper addresses the challenge of reachable set estimation in singular multi-agent systems with time-delay and semi-Markov switching topologies. The primary goal is to construct an ellipsoid that encompasses all states originating from the origin. Furthermore, criteria for reachable sets with reduced conservatism are developed. The singular multi-agent systems are reduced by the Laplace matrix, then the Lyapunov–Krasovskii functional is constructed by using decomposed state vector. In addition, the sufficient conditions for the reachable set of singular multi-agent systems bounded by ellipsoids are given by matrix theory and linear matrix inequality. Numerical examples show that our results is effective.

本文探讨了在具有时间延迟和半马尔可夫切换拓扑的奇异多代理系统中进行可达集估计的难题。其主要目标是构建一个椭圆体，该椭圆体包含从原点出发的所有状态。此外，还制定了减少保守性的可达集标准。奇异多代理系统由拉普拉斯矩阵还原，然后利用分解的状态向量构建 Lyapunov-Krasovskii 函数。此外，还通过矩阵理论和线性矩阵不等式给出了奇异多代理系统的可到达集以椭圆为界的充分条件。数值实例表明我们的结果是有效的。

引用次数: 0

Event-triggered finite-time fault-tolerance control and simultaneous disturbance rejection for Markov jump systems with general transition probabilities 具有一般过渡概率的马尔可夫跃迁系统的事件触发有限时间容错控制和同步干扰抑制

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-24 DOI: 10.1016/j.jfranklin.2024.107292

Xiang Pan , Guochen Pang , Xiangyong Chen , Yang Liu , Jianlong Qiu , Jinde Cao

This paper employs an event-triggered control approach to investigate the simultaneous finite-time fault-tolerant control and disturbance rejection problem for stochastic Markovian jump systems with general transition probabilities. Firstly, in conjunction with the event-triggered mechanism, a novel composite observer is designed, which can not only simultaneously estimate states and faults as well as disturbances of the system, but also guarantee that the error system is stochastically finite-time bounded. Subsequently, leveraging the obtained estimations, an active fault-tolerant controller with anti-disturbance performance is constructed to guarantee the finite-time boundedness of closed-loop system. Finally, the effectiveness of the proposed scheme is verified by distinct examples.

本文采用事件触发控制方法，研究了具有一般过渡概率的随机马尔可夫跃迁系统的同时有限时间容错控制和干扰抑制问题。首先，结合事件触发机制，设计了一种新型复合观测器，它不仅能同时估计系统的状态、故障和干扰，还能保证误差系统是随机有限时间约束的。随后，利用所获得的估计结果，构建了具有抗干扰性能的主动容错控制器，以保证闭环系统的有限时间约束性。最后，通过不同的实例验证了所提方案的有效性。

引用次数: 0

Output synchronization of a class of complex dynamic networks: A reinforcement learning method 一类复杂动态网络的输出同步：一种强化学习方法

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-24 DOI: 10.1016/j.jfranklin.2024.107284

Ning Zheng, Jinxu Liu, Lei Su, Shaoyu Lv, Hao Shen

In this paper, to achieve the synchronization control for a class of complex dynamic networks with completely unknown system dynamics, a reinforcement learning output feedback algorithm based on state reconstruction is proposed. Given the high cost and complexity associated with obtaining the full state information, an output-based node state reconstruction method is employed for the first time in complex dynamic networks. The proposed method utilizes a sequence composed of a finite number of output data to reconstruct the current state. At the same time, the overall error system is constructed to handle the coupling relationship between nodes, to facilitate the controller design. Thereafter, considering the system dynamics are unknown, an algorithm based on reinforcement learning is proposed to ensure rapid synchronization of node outputs, and the convergence of proposed method is proven. Finally, the feasibility of proposed algorithm is corroborated through a simulation example and a multi-vehicle system.

本文提出了一种基于状态重构的强化学习输出反馈算法，以实现对一类系统动态完全未知的复杂动态网络的同步控制。考虑到获取完整状态信息的高成本和复杂性，本文首次在复杂动态网络中采用了基于输出的节点状态重构方法。所提出的方法利用由有限数量的输出数据组成的序列来重建当前状态。同时，为处理节点间的耦合关系，构建了整体误差系统，以方便控制器的设计。随后，考虑到系统动态是未知的，提出了一种基于强化学习的算法，以确保节点输出的快速同步，并证明了所提方法的收敛性。最后，通过一个仿真实例和一个多车辆系统证实了所提算法的可行性。

引用次数: 0

Fusion estimation for state-saturated multi-rate systems with integral measurement and sensor resolution 具有积分测量和传感器分辨率的状态饱和多速率系统的融合估计

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-24 DOI: 10.1016/j.jfranklin.2024.107281

Long Xu , Changcheng Shi , Hui Yu , Ling Hou

In this paper, the fusion estimation problem is studied for a class of multi-rate systems (MRS) subject to state saturation. The state update rate of the system and the sampling rate of sensors are different in MRS. In addition, to incorporate the requirements of actual engineering practice, the sensor resolution and the phenomenon of integral measurements are considered in the measurement output of sensors. The aim of this paper is to design an appropriate set of local estimators for each sensor node by taking into account the effects of integral measurement, sensor resolution and multi-rate sampling mechanism. Then, the upper bound on the error covariance of the estimator can be obtained by calculating solution of the matrix difference equation and the relevant filter parameter is determined by minimizing the upper bound. Based on the covariance intersection (CI) method, the fusion estimation is obtained by fusing local estimates. The consistency is demonstrated for this fusion estimating approach. Finally, the effectiveness and appropriateness of the multi-rate fusion estimation approach are confirmed by comprehensive simulations.

本文研究了一类受状态饱和影响的多速率系统（MRS）的融合估计问题。在 MRS 中，系统的状态更新率和传感器的采样率是不同的。此外，为了结合工程实际的要求，还考虑了传感器测量输出中的传感器分辨率和积分测量现象。本文旨在考虑积分测量、传感器分辨率和多速率采样机制的影响，为每个传感器节点设计一套合适的局部估计器。然后，可以通过计算矩阵差分方程的解来获得估计器误差协方差的上界，并通过最小化上界来确定相关的滤波器参数。基于协方差交集（CI）方法，通过融合局部估计值获得融合估计值。这种融合估计方法的一致性得到了证明。最后，通过综合模拟证实了多速率融合估计方法的有效性和适当性。

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引用次数: 0

Projective lag synchronization of fractional delayed memristive neural networks via event-based hybrid pinning impulsive controller 通过基于事件的混合针刺脉冲控制器实现分数延迟记忆神经网络的投影滞后同步

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-24 DOI: 10.1016/j.jfranklin.2024.107297

Huiyu Wang , Shutang Liu , Xiang Wu , Wei Qiao , Jie Sun

This paper delves into the projective lag synchronization of Riemann–Liouville type fractional-order memristive neural networks accounting for jump mismatch. Recognizing the inherent inconsistencies in synchronizing traditional fractional-order memristive neural networks, we introduce a novel mathematical model that accommodates the jump mismatch phenomenon. A groundbreaking event-based hybrid pinning impulsive controller is then introduced, equipped with tailored event-triggering conditions, to elucidate the global asymptotic projective lag synchronization. Leveraging inequality principles and impulse analysis, a new Lyapunov functional is proposed, formulating sufficient conditions for synchronization while theoretically eliminating Zeno behavior in the controller. Notably, our approach substantially optimizes control overhead while fulfilling practical synchronization requisites. In addition, the obtained sufficient conditions can theoretically guide practical engineering applications of the network. Finally, a simulation example, emphasizing varied projective and lag factors, demonstrates our findings.

本文深入探讨了黎曼-刘维尔型分数阶忆苦思甜神经网络的投影滞后同步问题，并考虑了跳跃错配现象。我们认识到传统分数阶忆苦思甜神经网络在同步方面存在固有的不一致性，因此引入了一种新的数学模型，以适应跳跃失配现象。然后，我们引入了一种开创性的基于事件的混合针刺脉冲控制器，该控制器配备了量身定制的事件触发条件，以阐明全局渐近投影滞后同步。利用不等式原理和脉冲分析，提出了一种新的 Lyapunov 函数，为同步提出了充分条件，同时从理论上消除了控制器中的 Zeno 行为。值得注意的是，我们的方法在满足实际同步要求的同时，大大优化了控制开销。此外，所获得的充分条件还能从理论上指导网络的实际工程应用。最后，一个强调不同投影和滞后因素的仿真实例证明了我们的发现。

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引用次数: 0

Prescribed-time practical stabilization via majorant systems 通过主系统实现规定时间内的实际稳定

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics

Pub Date : 2024-09-23 DOI: 10.1016/j.jfranklin.2024.107298

Laura Celentano , Alison Garza-Alonso , Michael V. Basin

This paper provides a design methodology of simple control laws to solve the prescribed-time practical stabilization problem for a class of pseudo-linear systems with a single input, also unstable, subject to perturbations, nonlinear and discontinuous, but bounded. The perturbations on the dynamic matrix, such as a ratio of multi-affine polynomials of nonlinear functions or parameters, are not triangular, and a perturbation on the input of multiplicative type is also considered. The stated methodology is based on the majorant systems approach with optimized quadratic Lyapunov functions. The main advantages of the proposed control laws consist in the simplicity of their design and implementation and the reliable performance. Indeed, these control laws depend on a single design parameter, have constant gains, and guarantee the practical convergence with a prescribed relative error, also after a prescribed time. Some examples, also an engineering one, are provided to validate the obtained theoretical results and show the effectiveness and efficiency of the proposed control laws.

本文提供了一种简单控制律的设计方法，用于解决一类具有单一输入的伪线性系统的规定时间实际稳定问题，该系统也是不稳定的，受到非线性、不连续但有界的扰动。动态矩阵上的扰动，如非线性函数或参数的多芬多项式之比，不是三角形的，而且还考虑了乘法类型的输入扰动。所述方法基于具有优化二次 Lyapunov 函数的主要系统方法。所提控制法则的主要优势在于设计和实施简单，性能可靠。事实上，这些控制法则只依赖于一个设计参数，具有恒定增益，并能保证在规定的相对误差和规定的时间内实际收敛。我们还提供了一些工程实例，以验证所获得的理论结果，并展示所提出的控制法则的有效性和效率。

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