Article
Mathematics, Applied
Xing -Yu Li, Kai-Ning Wu, Xiao-Zhen Liu
Summary: In this study, the Mittag-Leffler stabilization of short memory fractional reaction-diffusion systems (SMFRDSs) is investigated using a designed intermittent boundary controller. By employing the Lyapunov functional method and various inequalities, a sufficient criterion is derived to ensure the Mittag-Leffler stability of SMFRDSs. The robust Mittag-Leffler stability is also considered in the presence of uncertainties in SMFRDSs. Furthermore, the influence of control gains and diffusion coefficient matrix on stability is analyzed. Numerical simulations are conducted to validate the proposed approach based on the obtained results.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Mathematics, Applied
Amin Boumenir, Vu Kim Tuan
Summary: It is proven that the coefficients of a one-dimensional fractional diffusion equation can be uniquely recovered from a single boundary measurement with a constructive procedure provided. The algorithm is built upon the Gelfand-Levitan inverse spectral theory of Sturm-Liouville operators. The nonlocal nature of the fractional derivative poses challenges in observing the solution and extracting spectral data.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2021)
Article
Mathematics, Interdisciplinary Applications
Jingjing Zeng, Xujun Yang, Lu Wang, Xiaofeng Chen
Summary: This paper investigates the robust asymptotical stability and stabilization for a class of fractional-order complex-valued neural networks with parametric uncertainties and time delay. The system combines complex numbers, uncertain parameters, time delay, and fractional orders, making it universal in practical application. The results include the sufficient conditions for the existence and uniqueness of equilibrium points, as well as criteria for robust asymptotical stability and stabilization of the addressed models. Two numerical examples are provided to validate the theoretical results.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2021)
Article
Mathematics, Applied
Anumanthappa Ganesh, Swaminathan Deepa, Dumitru Baleanu, Shyam Sundar Santra, Osama Moaaz, Vediyappan Govindan, Rifaqat Ali
Summary: In this paper, we discuss the standard approaches to the Hyers-Ulam Mittag Leffler problem of fractional derivatives and nonlinear fractional integrals using a fractional Fourier transform. We prove the basic properties of derivatives, provide a brief method for solving linear fractional differential equations, and derive the structure of the Hyers-Ulam Mittag Leffler problem for linear two-term equations. Additionally, we consider some physical examples.
Article
Engineering, Mechanical
Oscar Martinez-Fuentes, Aldo Jonathan Munoz-Vazquez, Guillermo Fernandez-Anaya, Esteban Tlelo-Cuautle
Summary: In this paper, a class of dynamic observers for nonlinear fractional-order systems is studied, and the Mittag-Leffler stability is analyzed. The Riemann-Liouville integral is utilized to provide robustness against noisy measurements, and a family of high gain proportional rho-integral observers is designed for estimating unmeasured state variables.
NONLINEAR DYNAMICS
(2023)
Article
Mathematics, Applied
Nasser-eddine Tatar
Summary: The study investigates the use of non-integer derivatives in the telegraph problem, showing that both the low-order fractional derivative and the viscoelastic term can stabilize the system and are of Mittag-Leffler type. However, in the fractional case, some basic rules no longer apply, making the situation more delicate. Mittag-Leffler stability is proven under certain smallness conditions on the relaxation function.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2021)
Article
Mathematics, Interdisciplinary Applications
Nasser-eddine Tatar
Summary: The stability of a fractional order Euler-Bernoulli type problem was investigated in this research. By adding lower-order fractional term and a memory term, it was shown that the system can be stabilized to rest in a Mittag-Leffler manner. The results depend heavily on established properties of fractional derivatives and newly introduced functionals.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics, Interdisciplinary Applications
Rahat Zarin, Amir Khan, Mustafa Inc, Usa Wannasingha Humphries, Touria Karite
Summary: This article discusses the transmission of Anthroponotic Cutaneous Leishmania and develops a mathematical model to analyze its qualitative behavior. The threshold number R-0 of the model is derived using the next generation method, and sensitivity analysis with other parameters is also considered. Several graphs of important parameters are discussed in the article.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics, Interdisciplinary Applications
Banan Al-Homidan, Nasser-eddine Tatar
Summary: This paper investigates a non-linear fractional equation between one and two, which combines features of both the heat and wave equations. The study focuses on stabilizing the system using a lower-order fractional term or a memory term involving the Laplacian. Global and local stability results are proven under different conditions. The challenges in this case mainly stem from the memory dependence of the fractional derivatives, which invalidates the product rule.
FRACTAL AND FRACTIONAL
(2023)
Article
Engineering, Multidisciplinary
Ebenezer Bonyah, Mehmet Yavuz, Dumitru Baleanu, Sunil Kumar
Summary: Listeriosis is a zoonotic disease that affects many Sub-Saharan countries, and a mathematical model incorporating fractal-fractional orders has been developed in this paper to investigate the disease's future behaviors. The study reveals that numerical schemes are effective for predicting and analyzing complex phenomena, providing insights into the disease's spread and steady states.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Mathematics, Applied
Alireza Ansari, Mohammad Hossein Derakhshan, Hassan Askari
Summary: In this paper, we study the powers of the Laplacian operator in axisymmetric cylindrical geometry, obtaining the fundamental solution of the corresponding distributed order time-fractional diffusion equation. We also discuss the roles of the Mittag-Leffler and Wright functions in the structures of solutions.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Physics, Multidisciplinary
Ajendra Singh, Jitendra Nath Rai
Summary: In this paper, the stability of fractional order fuzzy cellular neural networks with leakage delay and time varying delays was investigated. Sufficient criteria were established to guarantee stability based on Lyapunov theory and bounded techniques of fractional calculation. Hybrid feedback control was applied to derive the proposed results, and numerical examples with simulation results were used to illustrate the effectiveness of the method.
CHINESE JOURNAL OF PHYSICS
(2021)
Article
Mathematics, Applied
Oscar Martinez-Fuentes, Guillermo Fernandez-Anaya, Aldo Jonathan Munoz-Vazquez
Summary: Stability analysis is crucial in control systems design. This paper focuses on fractional systems modeled by the Atangana-Baleanu derivative, introducing novel inequalities and considering quadratic and convex Lyapunov functions for stability analysis using the Direct Lyapunov Method.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2021)
Article
Mathematics, Applied
Sachin Kumar, Dia Zeidan
Summary: This paper presents the fractional formulation and numerical solution of a non-linear fractional diffusion equation with advection and reaction terms using the Legendre operational matrix. The feasibility of the proposed derived ABC fractional differentiation operational matrix is validated through numerical resolutions against exact solutions, demonstrating its capability to resolve non-linear fractional diffusion equations of different fractional orders.
APPLIED NUMERICAL MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Shenglong Chen, Hong-Li Li, Haibo Bao, Long Zhang, Haijun Jiang, Zhiming Li
Summary: This paper investigates discrete-time fractional-order delayed quaternion-valued neural networks (DFDQNNs) using the direct quaternion approach. A novel lemma and its corresponding corollaries are proposed for estimating the nabla fractional difference of the quaternion-valued Lyapunov function. The existence and uniqueness of equilibrium point for DFDQNNs are proved by constructing a new quaternion-valued contraction mapping. Sufficient criteria for global Mittag-Leffler stability and Mittag-Leffler synchronization of DFDQNNs are obtained using designed Lyapunov functions, effective feedback controller, and neoteric nabla difference inequalities. Numerical examples are provided to verify the results.
Article
Automation & Control Systems
Yun-Zhu Wang, Zhen Wang, Kai-Ning Wu, Chen-Xu Wang
Summary: This article discusses the exponential stabilization and H-infinity performance of delay reaction-diffusion systems with spatial and spatio-temporal sampled-data controllers. Criteria for stability and disturbance rejection are provided, and a novel Lyapunov functional and Halaney's inequality are used to overcome analysis difficulties. Results show that spatial sampling interval and time delay both affect system properties, with shorter intervals and smaller delays leading to easier achievement of desired stability properties.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Xiao-Zhen Liu, Kai-Ning Wu, Ze-Tao Li
Summary: This paper studies the exponential stabilization of reaction-diffusion systems (RDSs) with a reaction term satisfying the global Lipschitz condition. Two methods are proposed to achieve system stability by designing intermittent boundary controllers and observers, and a robust controller is also introduced to handle system uncertainties.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Xin-Xin Han, Kai-Ning Wu, Yu Yao
Summary: This paper deals with the exponential boundary stabilization of a class of Markov jump reaction diffusion neural networks with mixed time-varying delays. A novel asynchronous boundary control law is developed using observed modes, and a sufficient condition for the stability of the system is established. The results of this study are important for understanding control strategies for distributed parameter systems.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Kai-Ning Wu, Wei-Jie Zhou, Xiao-Zhen Liu
Summary: This paper investigates the passivity-based boundary control problem of reaction-diffusion systems with time-varying delay and boundary input-output. By employing the Lyapunov functional method and inequality techniques, sufficient conditions for input strict passivity and output strict passivity of the systems are derived. In the presence of parameter uncertainties, sufficient conditions for robust passivity are presented. Moreover, the theoretical results are applied to the synchronization problem of coupled reaction-diffusion systems with delay, and a criterion for asymptotic synchronization is obtained. Numerical simulations are provided to validate the effectiveness of the theoretical results.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Engineering, Mechanical
Shuang Liang, Kai-Ning Wu
Summary: The boundary control problem for stochastic Korteweg-de Vries-Burgers equations is investigated, with proposed criteria for mean square exponential stability, robust mean square exponential stability, and mean square H-infinity performance, in the presence of uncertainties in system parameters and additive noises. Numerical examples validate the theoretical results.
NONLINEAR DYNAMICS
(2022)
Article
Computer Science, Artificial Intelligence
Xing-Yu Li, Qing-Ling Fan, Xiao-Zhen Liu, Kai-Ning Wu
Summary: This article investigates the exponential stability of delay reaction-diffusion cellular neural networks (DRDCNNs) in two cases: when the state information is fully available and when it is not fully available. Aperiodically intermittent boundary controllers are designed to stabilize the controlled system when the state information is fully available, and observer-based aperiodically intermittent boundary controllers are proposed when the state information is not fully available. By utilizing the Lyapunov functional method and Poincare's inequality, a criterion for achieving exponential stabilization of DRDCNNs is obtained. The influence of diffusion coefficient matrix, control gains, time-delays, and control proportion on stability is studied based on the obtained results. Numerical examples are presented to illustrate the effectiveness of the theoretical results.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xiao-Zhen Liu, Kai-Ning Wu, Xiaohua Ding, Weihai Zhang
Summary: This study focuses on the boundary stabilization of stochastic delayed Cohen-Grossberg neural networks with diffusion terms by using boundary control for mean-square exponential stabilization. The effects of diffusion matrix, coupling strength, and time delays on exponentially stability are analyzed, and Poincare's inequality and Schur's complement lemma are used to address the difficulties in system analysis. Additionally, an application of the theoretical result is presented for mean-square exponential synchronization of stochastic delayed Hopfield neural networks under boundary control.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Wei-Jie Zhou, Min Long, Xiao-Zhen Liu, Kai-Ning Wu
Summary: This paper investigates the passivity-based boundary control problem for stochastic delay reaction-diffusion systems with boundary input-output. Delay-dependent sufficient conditions are obtained to ensure the stability and robustness of the system using Lyapunov functional method and stochastic inequality techniques. Numerical simulations are provided to validate the effectiveness of the proposed theoretical results.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Mathematics, Applied
Run-Jie Zhang, Liming Wang, Kai-Ning Wu
Summary: This paper investigates the boundary finite-time stabilization of fractional reaction-diffusion systems (FRDSs). Sufficient conditions are obtained to ensure the finite-time stability (FTS) of FRDSs under the designed controller. The effect of diffusion term of FRDSs on the FTS is also investigated. Both Neumann and mixed boundary conditions are considered. Moreover, the robust finite-time stabilization of uncertain FRDSs is studied when there are uncertainties in the system's coefficients. Numerical examples are presented to verify the effectiveness of the theoretical results.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Mathematics, Applied
Shuang Liang, Kai-Ning Wu, Ming-Xin He
Summary: The research focuses on the finite-time boundary stabilization of the Korteweg-de Vries-Burgers (KdVB) equations. A distributed controller and a boundary controller design are proposed to ensure stability. The effectiveness of the proposed methods is verified through theoretical analysis and numerical examples.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Mathematics, Applied
Shuang Liang, Deqiong Ding, Kai-Ning Wu
Summary: The exponential input-to-state stability (EISS) for delay Korteweg-de Vries-Burgers (DKdVB) equations is investigated in this paper. By using the Lyapunov-Krasovskii functional method and inequality techniques, a sufficient condition is established to ensure the EISS for DKdVB equations. This condition shows the effect of both time delay and diffusion term on the EISS. Robust EISS of uncertain DKdVB equations is also studied in the presence of uncertainties of system's coefficients, and a criterion is obtained to guarantee the EISS for the uncertain DKdVB equation. Numerical simulation examples are provided to demonstrate the validity of the derived results.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Automation & Control Systems
Xiao-Zhen Liu, Kai-Ning Wu, Choon Ki Ahn
Summary: This article studies the synchronization problem of coupled fractional delayed reaction-diffusion neural networks with boundary controllers. The study presents both time-continuous and time-discontinuous controllers and analyzes the effects of control parameters on system performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Mathematics, Applied
Xing -Yu Li, Kai-Ning Wu, Xiao-Zhen Liu
Summary: In this study, the Mittag-Leffler stabilization of short memory fractional reaction-diffusion systems (SMFRDSs) is investigated using a designed intermittent boundary controller. By employing the Lyapunov functional method and various inequalities, a sufficient criterion is derived to ensure the Mittag-Leffler stability of SMFRDSs. The robust Mittag-Leffler stability is also considered in the presence of uncertainties in SMFRDSs. Furthermore, the influence of control gains and diffusion coefficient matrix on stability is analyzed. Numerical simulations are conducted to validate the proposed approach based on the obtained results.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Xin-Xin Han, Kai-Ning Wu, Yugang Niu
Summary: This article presents an asynchronous boundary control design for a class of MJRDNNs, establishes a sufficient criterion for ensuring the stochastic finite-time boundedness of the considered MJRDNNs, and provides a numerical example to illustrate the effectiveness of the proposed design method.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Hamdan Abdellatef, Lina J. Karam
Summary: This paper proposes performing the learning and inference processes in the compressed domain to reduce computational complexity and improve speed of neural networks. Experimental results show that modified ResNet-50 in the compressed domain is 70% faster than traditional spatial-based ResNet-50 while maintaining similar accuracy. Additionally, a preprocessing step with partial encoding is suggested to improve resilience to distortions caused by low-quality encoded images. Training a network with highly compressed data can achieve good classification accuracy with significantly reduced storage requirements.
Article
Computer Science, Artificial Intelligence
Victor R. Barradas, Yasuharu Koike, Nicolas Schweighofer
Summary: Inverse models are essential for human motor learning as they map desired actions to motor commands. The shape of the error surface and the distribution of targets in a task play a crucial role in determining the speed of learning.
Article
Computer Science, Artificial Intelligence
Ting Zhou, Hanshu Yan, Jingfeng Zhang, Lei Liu, Bo Han
Summary: We propose a defense strategy that reduces the success rate of data poisoning attacks in downstream tasks by pre-training a robust foundation model.
Article
Computer Science, Artificial Intelligence
Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao
Summary: In this paper, the convergence rate of AdaSAM in the stochastic non-convex setting is analyzed. Theoretical proof shows that AdaSAM has a linear speedup property and decouples the stochastic gradient steps with the adaptive learning rate and perturbed gradient. Experimental results demonstrate that AdaSAM outperforms other optimizers in terms of performance.
Article
Computer Science, Artificial Intelligence
Juntong Yun, Du Jiang, Li Huang, Bo Tao, Shangchun Liao, Ying Liu, Xin Liu, Gongfa Li, Disi Chen, Baojia Chen
Summary: In this study, a dual manipulator grasping detection model based on the Markov decision process is proposed. By parameterizing the grasping detection model of dual manipulators using a cross entropy convolutional neural network and a full convolutional neural network, stable grasping of complex multiple objects is achieved. Robot grasping experiments were conducted to verify the feasibility and superiority of this method.
Article
Computer Science, Artificial Intelligence
Miaohui Zhang, Kaifang Li, Jianxin Ma, Xile Wang
Summary: This paper proposes an unsupervised person re-identification (Re-ID) method that uses two asymmetric networks to generate pseudo-labels for each other by clustering and updates and optimizes the pseudo-labels through alternate training. It also designs similarity compensation and similarity suppression based on the camera ID of pedestrian images to optimize the similarity measure. Extensive experiments show that the proposed method achieves superior performance compared to state-of-the-art unsupervised person re-identification methods.
Article
Computer Science, Artificial Intelligence
Florian Bacho, Dominique Chu
Summary: This paper proposes a new approach called the Forward Direct Feedback Alignment algorithm for supervised learning in deep neural networks. By combining activity-perturbed forward gradients, direct feedback alignment, and momentum, this method achieves better performance and convergence speed compared to other local alternatives to backpropagation.
Article
Computer Science, Artificial Intelligence
Xiaojian Ding, Yi Li, Shilin Chen
Summary: This research paper addresses the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. The proposed algorithms, which introduce a novel feature ranking criterion and an optimal feature subset evaluation algorithm, outperform current state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima
Summary: Visual images observed by humans can be reconstructed from brain activity, and the visualization of arbitrary natural images from mental imagery has been achieved through an improved method. This study provides a unique tool for directly investigating the subjective contents of the brain.
Article
Computer Science, Artificial Intelligence
Huanjie Tao, Qianyue Duan
Summary: In this paper, a hierarchical attention network with progressive feature fusion is proposed for facial expression recognition (FER), addressing the challenges posed by pose variation, occlusions, and illumination variation. The model achieves enhanced performance by aggregating diverse features and progressively enhancing discriminative features.
Article
Computer Science, Artificial Intelligence
Zhenyi Wang, Pengfei Yang, Linwei Hu, Bowen Zhang, Chengmin Lin, Wenkai Lv, Quan Wang
Summary: In the face of the complex landscape of deep learning, we propose a novel subgraph-level performance prediction method called SLAPP, which combines graph and operator features through an innovative graph neural network called EAGAT, providing accurate performance predictions. In addition, we introduce a mixed loss design with dynamic weight adjustment to improve predictive accuracy.
Article
Computer Science, Artificial Intelligence
Yiyang Yin, Shuangling Luo, Jun Zhou, Liang Kang, Calvin Yu-Chian Chen
Summary: Medical image segmentation is crucial for modern healthcare systems, especially in reducing surgical risks and planning treatments. Transanal total mesorectal excision (TaTME) has become an important method for treating colon and rectum cancers. Real-time instance segmentation during TaTME surgeries can assist surgeons in minimizing risks. However, the dynamic variations in TaTME images pose challenges for accurate instance segmentation.
Article
Computer Science, Artificial Intelligence
Teng Cheng, Lei Sun, Junning Zhang, Jinling Wang, Zhanyang Wei
Summary: This study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, called Fast Spectrum-Size Self-Training network (FSSNet). By utilizing start-stop points to build the signal model, this method successfully solves the difficulty of existing deep learning methods in detecting discontinuous signals and achieves satisfactory detection speed.
Article
Computer Science, Artificial Intelligence
Wenming Wu, Xiaoke Ma, Quan Wang, Maoguo Gong, Quanxue Gao
Summary: The layer-specific modules in multi-layer networks are critical for understanding the structure and function of the system. However, existing methods fail to accurately characterize and balance the connectivity and specificity of these modules. To address this issue, a joint learning graph clustering algorithm (DRDF) is proposed, which learns the deep representation and discriminative features of the multi-layer network, and balances the connectivity and specificity of the layer-specific modules through joint learning.
Article
Computer Science, Artificial Intelligence
Guanghui Yue, Guibin Zhuo, Weiqing Yan, Tianwei Zhou, Chang Tang, Peng Yang, Tianfu Wang
Summary: This paper proposes a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. BUNet utilizes a pyramid vision transformer encoder to learn multi-scale features and incorporates a boundary exploration module (BEM) and a boundary uncertainty aware module (BUM) to handle boundary areas. Experimental results demonstrate that BUNet outperforms other methods in terms of performance and generalization ability.
