Article
Computer Science, Artificial Intelligence
Xiaojin Fan, Mengmeng Liao, Jingfeng Xue, Hao Wu, Lei Jin, Jian Zhao, Liehuang Zhu
Summary: In this paper, a Joint Coupled Representation and Homogeneous Reconstruction (JCRHR) method is proposed for multi-resolution small sample face recognition. The method improves the coherent representation of coding coefficients and the reconstruction effect of samples at different resolutions by introducing an analysis dictionary, a synthetic dictionary, and a coherence enhancement term. Experimental results demonstrate that the proposed JCRHR method outperforms existing methods on several small sample face databases.
Article
Computer Science, Artificial Intelligence
Haishun Du, Yonghao Zhang, Luogang Ma, Fan Zhang
Summary: The SDADL method proposes a structured discriminant analysis dictionary learning approach to improve pattern classification by associating class-specific analysis sub-dictionaries. It introduces classification error term, discriminant analysis sparse code error term, and structured discriminant term to optimize the dictionary learning process, along with designing an efficient iterative algorithm for optimization.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Gihwan Lee, Yoonsik Choe
Summary: This paper proposes an extension of a sparse orthonormal transform based on unions of orthonormal dictionaries for image compression. The method constructs dictionaries into a discrete cosine transform and an orthonormal matrix, and adapts Bayesian optimization to determine a trade-off parameter for efficient implementation and optimal parameter selection.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Ali Nozaripour, Hadi Soltanizadeh
Summary: Convolutional Sparse Coding (CSC) is a popular model in signal and image processing, and this paper proposes a novel discriminative model based on CSC for image classification. Experimental results demonstrate the superior performance of the proposed method.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Software Engineering
Qiao Du, Feipeng Da
Summary: The paper introduces a novel approach called block dictionary learning (BDL) which combines sparse representation and convolutional neural networks to address the fewshot face recognition problem. Through local feature extraction and a global-to-local dictionary learning algorithm, BDL demonstrates effectiveness in comparison with other FFR methods on AR and Extended Yale B datasets.
Article
Computer Science, Information Systems
Haishun Du, Yonghao Zhang, Yuxi Wang, Linbing He
Summary: In this study, a double-constrained structured discriminant analysis-synthesis dictionary pair learning method is proposed to address the issues existing in existing discriminant ASDPL methods. By incorporating reconstruction and independence constraints, the proposed method enhances the representational and discriminative ability of learned dictionary pairs. Experimental results demonstrate the effectiveness of the method in pattern classification.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mohamad Dhaini, Maxime Berar, Paul Honeine, Antonin Van Exem
Summary: This paper studies the problem of unsupervised domain adaptation for regression tasks and proposes a new approach based on dictionary learning. Experimental results show that the proposed method outperforms most of state-of-the-art methods on several benchmark datasets, especially when transferring knowledge from synthetic to real domains.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xinxin Shan, Yue Lu, Qingli Li, Ying Wen
Summary: The paper introduces a framework for partial face recognition based on model-based transfer learning and sparse coding, demonstrating its efficacy through experimental results.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Chemistry, Analytical
Xu Zhang, Qifeng Liu, Dong He, Hui Suo, Chun Zhao
Summary: This paper proposes an approach for identifying individuals through their ECG signals, using mixed feature sampling, sparse representation, and recognition. The method utilizes wavelet transform for feature extraction and sparse dictionary creation, achieving high recognition rates. The experiments validate the accuracy and effectiveness of the proposed method.
Article
Computer Science, Information Systems
Keke Huang, Haofei Wen, Han Liu, Chunhua Yang, Weihua Gui
Summary: Data-driven process monitoring methods rely on geometry constrained dictionary learning to balance reconstructive and discriminative items. Inspired by the manifold method, discriminative sparse coding is employed to identify samples from the same class.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Yuxi Wang, Haishun Du, Yonghao Zhang, Yanyu Zhang
Summary: This study proposes an efficient and robust discriminant analysis-synthesis dictionary pair learning method for pattern classification. The method designs a coding coefficient discriminant term and imposes a low-rank constraint to enhance the discrimination capability of the structured analysis dictionary and weaken the influence of noises on the structured synthesis dictionary. Experimental results demonstrate that the method has higher classification accuracy and efficiency compared to state-of-the-art dictionary learning methods.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Computer Science, Information Systems
Yue Pan, Tianye Lan, Chongyang Xu, Chengfang Zhang, Ziliang Feng
Summary: This paper reviews the recent advances in pixel-level image fusion based on convolutional sparse representation (CSR) and discusses the future trends of CSR-based image fusion.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Mengmeng Liao, Xiaojin Fan, Yan Li, Meiguo Gao
Summary: A novel noise-related face image recognition method based on double dictionary transform learning (DDTL) is proposed in this paper. The method removes the redundant information and noise in the training images, making the learned dictionary more discriminative. It also introduces a linear regression term to enhance the differences between classes. Experimental results demonstrate that the proposed method outperforms existing methods.
INFORMATION SCIENCES
(2023)
Article
Engineering, Mechanical
Yanglong Lu, Yan Wang
Summary: The new technique of physics-constrained dictionary learning aims to reduce the volume of data in storage and communication using compressed sensing, while maintaining the amount of information collection.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Juan E. Arco, Andres Ortiz, Javier Ramirez, Yu-Dong Zhang, Juan M. Gorriz
Summary: The study introduces a classification framework for medical image diagnosis based on sparse coding, which successfully distinguishes between different pathological types and achieves excellent performance in a real context.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Engineering, Civil
Hao Zhang, Juan Liu, Zhuping Wang, Huaicheng Yan, Changzhu Zhang
Summary: This paper proposes a novel control framework for the vehicular platoon, including a distributed adaptive event-triggered observer and a car-following control protocol, to ensure all vehicles travel at the same speed and maintain safety spacing. By designing a distributed event-triggered observer that considers collision avoidance and limited communication source for each vehicle, the stability of the platoon is achieved.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Xiaoyuan Zheng, Hao Zhang, Zhuping Wang, Chao Huang, Huaicheng Yan
Summary: This study investigates the problem of stochastic event-based distributed fusion estimation for a class of Gaussian systems, and proposes a two-step fusion estimation method based on stochastic event-triggered mechanisms. By considering channel fading and designing a fusion algorithm, the goal of eliminating discrepancies among local sensor estimations is achieved.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2022)
Article
Automation & Control Systems
Chao Huang, Linlin Zhang, Changzhu Zhang, Hao Zhang
Summary: In this paper, a robust predictor feedback controller design is proposed for enlarging the admissible delay mismatch while maintaining the convergence rate within a prescribed level. The controller synthesis problem is shown to be cast into a generalised eigenvalue problem (GEVP).
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Chao Huang, Gang Feng, Hao Zhang, Zhuping Wang
Summary: This article proposes a novel system identification method based on the notion of invariant subspace, with advantages including identifying linear continuous-time models from slowly sampled data, establishing consistency of model parameters, finding global optimum through solving linear least-square problems, and implementing identification algorithms online with explicit convergence rates.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Yongxiao Tian, Huaicheng Yan, Hao Zhang, Jun Cheng, Hao Shen
Summary: This article focuses on the output feedback control problem for continuous-time hidden semi-Markov jump systems with time delays. By establishing emission probabilities relationship, the asynchronous information between controller modes and system modes is better described, and new parameter-dependent stabilization conditions are proposed.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Yuan Wang, Huaicheng Yan, Hao Zhang, Hao Shen, Hak-Keung Lam
Summary: This article discusses the design problem of interval type-2 Takagi-Sugeno fuzzy asynchronous controller for nonlinear multiagent systems in discrete-time context. It proposes a dynamic event-triggered scheme to mitigate the communication burden. The article utilizes a unique IT2 T-S fuzzy model and a hidden Markov model to handle the asynchronous phenomena and nonlinear characteristics. The effectiveness and practicality of the proposed control scheme are demonstrated through examples.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Zhichen Li, Huaicheng Yan, Hao Zhang, Hak-Keung Lam, Congzhi Huang
Summary: In this article, a sampled-data ESO design approach is proposed for uncertain nonlinear systems. By using fuzzy modeling and Takagi-Sugeno fuzzy formulation, the nonlinear estimating efficiency and linear numerical tractability are integrated in a unified framework. An exponential convergence criterion for TSFESO is presented, and the observer design method is also provided. Numerical examples demonstrate the superiority and effectiveness of the proposed approaches.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Zhang, Yajie Zou, Xiaoxue Yang, Hang Yang
Summary: This study adopts a novel architecture called TFT to predict freeway speed, which can capture short-term and long-term temporal dependence and improve prediction accuracy by incorporating various types of inputs.
Article
Computer Science, Artificial Intelligence
Xindi Yang, Hao Zhang, Zhuping Wang
Summary: This article introduces a data-based distributed control algorithm to address the consensus control problem in multiagent systems, successfully overcoming the challenges of asynchronous learning. By incorporating an actor-critic structure and neural networks, the algorithm achieves convergence and optimality in both synchronous and asynchronous cases.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zhichen Li, Yu Zhao, Huaicheng Yan, Hao Zhang, Lu Zeng, Xiaolei Wang
Summary: This paper investigates the time-varying formation tracking control problem for multi-agent systems (MASs). The main objective is to achieve asymptotic convergence of formation tracking error despite nonparametric and nonvanishing uncertainties. A fuzzy extended state observer (FESO) based on event-triggered mechanism is proposed to estimate unmodeled dynamics and external disturbances. Furthermore, a distributed control law is developed using neighborhood formation tracking errors, and total disturbance compensation is introduced to attenuate uncertainty influence in real time. The effectiveness of the proposed control protocol is demonstrated using a numerical example on unmanned aerial vehicle swarm system.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Automation & Control Systems
Min Xue, Huaicheng Yan, Hao Zhang, Xisheng Zhan, Kaibo Shi
Summary: This article discusses compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. Utilizing single exponential smoothing as a compensation scheme, an asynchronous output feedback controller is designed with stochastic stability and strict dissipativity. Novel sufficient conditions for controller existence based on mode-dependent Lyapunov function are derived, along with an algorithm for determining the optimal smoothing parameter. Simulation results demonstrate the validity and advantages of the design approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Bo Qin, Huaicheng Yan, Hao Zhang, Yueying Wang, Simon X. Yang
Summary: This paper proposes a control method based on an enhanced reduced-order extended state observer for precise motion control in mobile robot systems. The method reduces energy consumption by estimating unknown state error and negative disturbance and uses a simple state-feedback-feedforward controller to track the reference signal and compensate for negative disturbance.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xindi Yang, Hao Zhang, Zhuping Wang, Huaicheng Yan, Changzhu Zhang
Summary: This article presents a model-free predictive control algorithm for real-time systems that improves system performance through data-driven multi-step policy gradient reinforcement learning. By learning from offline and real-time data, the algorithm avoids the need for knowledge of system dynamics in its design and application. Cooperative games are used to model predictive control as multi-agent optimization problems and ensure the optimal predictive control policy. Neural networks are employed to approximate the action-state value function and predictive control policy, with weights determined using weighted residual methods. Numerical results demonstrate the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhichen Li, Huaicheng Yan, Hao Zhang, Simon X. Yang, Mengshen Chen
Summary: This article investigates the design problem of an extended state observer (ESO) for uncertain nonlinear systems subject to limited network bandwidth. A dynamic event-triggered communication protocol is proposed for rational information exchange scheduling, achieving a desirable tradeoff between observation performance and communication resource efficiency. A novel paradigm of event-triggered Takagi-Sugeno fuzzy ESO is introduced, and the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics under the dynamic event-triggered mechanism. The effectiveness of the proposed method is verified through numerical examples, expanding the application scope of ESO with improved event-triggered strategies.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Yunkai Lv, Hao Zhang, Zhuping Wang, Huaicheng Yan
Summary: This article focuses on the real-time localization problem in dynamic multi-agent systems with measurement and communication noises under directed graphs. It introduces barycentric coordinates to describe the relative position between agents and proposes a novel robust distributed localization estimation algorithm based on iterative learning. The algorithm uses a relative-distance unbiased estimator constructed from historical iterative information to suppress measurement noise, and a designed stochastic approximation method with two iterative-varying gains to inhibit communication noise. The asymptotic convergence of the proposed methods is derived under certain conditions of zero-mean and independent distribution of measurement and communication noises. Numerical simulations and robot experiments are conducted to test and verify the effectiveness and practicability of the proposed methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
