A5106407478- Distributed Control Multi-Agent Systems
- Sparse and Compressive Sensing Techniques
- Stochastic Gradient Optimization Techniques
- Optimization and Variational Analysis
- Neural Networks and Applications
- Neural Networks and Reservoir Computing
- Privacy-Preserving Technologies in Data
- Privacy, Security, and Data Protection
- Cryptography and Data Security
- Advanced Wireless Communication Technologies
- Artificial Intelligence in Healthcare and Education
- Recommender Systems and Techniques
- Radiomics and Machine Learning in Medical Imaging
- Machine Learning and ELM
- Remote-Sensing Image Classification
- Parasitic infections in humans and animals
- Neural Networks Stability and Synchronization
- Congenital Anomalies and Fetal Surgery
- Mathematical and Theoretical Epidemiology and Ecology Models
- Parasitic Infections and Diagnostics
- Color Science and Applications
- Image Enhancement Techniques
- Target Tracking and Data Fusion in Sensor Networks
- Healthcare Systems and Public Health
- Infrared Target Detection Methodologies
Chongqing University
2021-2024
Southwest University
2017-2024
Beijing Tsinghua Chang Gung Hospital
2024
Northwestern Polytechnical University
2023
Bengbu Medical College
2021
Southeast University
2019
National Astronomical Observatories
2018
Chinese Academy of Sciences
2018
Chongqing University of Education
2017
This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -minimization problem. First, two are designed based on augmented Lagrange method with derivative feedback projection operator. Then, optimality global convergence of them derived. In addition,...
This article considers constrained nonsmooth generalized convex and strongly optimization problems. For such problems, two novel distributed smoothing projection neurodynamic approaches (DSPNAs) are proposed to seek their optimal solutions with faster convergence rates in a manner. First, we equivalently transform the original problem into standard only local set constraints based on an exact penalty approximation methods. Then, deal generally optimization, propose DSPNA continuous variant...
In this paper, a distributed smoothing accelerated projection algorithm (DSAPA) is proposed to address constrained non-smooth convex optimization problems over undirected multi-agent networks in manner, where the objective function free of assumption Lipschitz gradient or strong convexity. First, based on exact penalty method, original problem translated standard assignment without consensus constraints. Then, novel DSAPA by combining approximation with Nesterov's schemes proposed. addition,...
Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, resource allocation, etc. Existing decentralized algorithms require sharing explicit state information among the agents, which raises risk of private leakage. To ensure privacy security, combining security mechanisms, such as differential and homomorphic encryption, with traditional a commonly used means. However, this may either sacrifice accuracy or incur heavy computational burden....
In this paper, we investigate several distributed inertial algorithms in continuous and discrete time for solving resource allocation problem (RAP), where its objective function is convex or strongly convex. First, the original RAP equivalently transformed into a unconstrained optimization by introducing an auxiliary variable. Then, two are proposed rates of their convergence based on gap between optimal determined. Our first damped algorithm designed with function, it achieves rate at...
Sparsity has been extensively employed in multimedia sensing and computing consumer electronics, signal image processing, depth video codec, adaptive sparse-type equalizer, blind speech separation, machine learning. Throughout this paper, we propose a novel distributed projection neurodynamic approach for solving the Basis Pursuit (BP) with flexible partition methods manner. The proposed requires only that network is undirected connected, no node can access entire matrix simultaneously....
The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with convergence rates. Most previous studies in this field have primarily concentrated on unconstrained smooth problems. In paper, the basis of primal-dual approach, projection operator and directional gradient, we present two neurodynamic approaches time scaling to address nonsmooth objective functions subject linear set constraints, which consist a second-order ODE (ordinary...
Images captured under low-light conditions are characterized by lower visual quality and perception levels than images obtained in better lighting scenarios. Studies focused on enhancement techniques seek to address this dilemma. However, simple image brightening results significant noise, blurring, color distortion. In paper, we present a (LLE) solution that effectively synergizes Retinex theory with deep learning. Specifically, construct an efficient gradient map estimation module based...
Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, and resource allocation. Existing decentralized algorithms require sharing explicit state information among the agents, which raises risk of private leakage. To ensure privacy security, combining security mechanisms, such as differential homomorphic encryption, with traditional a commonly used means. However, this would either sacrifice accuracy or incur heavy computational burden....
By using combined tools from smooth approximation technique and exact penalty method, a distributed continuous-time algorithm is designed in this paper to solve kind of convex problem multi-agent networks with undirected topology. One the remarkable features lies fact that convergence rate $O(1 /t^{2})$ could be yielded by proposed if, some suitable conditions are satisfied. Specifically, deal optimization non-smooth cost functions or having non-Lipschitz gradient. The asymptotic property...
This paper investigates two accelerated primal-dual mirror dynamical approaches for smooth and nonsmooth convex optimization problems with affine closed, set constraints. In the case, an approach (APDMD) based on descent framework is proposed convergence properties of gap, feasibility measure objective function value along trajectories APDMD are derived by Lyapunov analysis method. Then, we extend into distributed to deal types problems, i.e., constrained consensus problem (DCCP) extended...
To analyze the components of proteins from Echinococcus granulosus cyst fluid using shotgun method, and to identify active with potential regulatory effects for immune dysregulation diseases.The E. was collected aseptically hepatic cysts patients cystic echinococcosis, characterized by liquid chromatography (LC) tandem mass spectrometry (MS/MS) following digestion trypsin. The protein data were searched software MaxQuant version 1.6.1.0 cellular components, molecular functions, biological...