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Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

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Journal of Systems science & Complexity 2024年第3期37卷 965-984页

作者： NIU Ben WANG Xiaoan WANG Xiaomei WANG Xinjun LI Tao Faculty of Electronic Information and Electrical Engineering Dalian University of TechnologyDalian 116024China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Mathematics Southeast UniversityNanjing 211189China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper,the authors propose an adaptive Barrier-Lyapunov-Functions(BLFs)based control scheme for nonlinear pure-feedback systems with full state *** to the coexist of the non-affine structure and full state constraints,it is very difficult to construct a desired controller for the considered *** to the mean value theorem,the authors transform the pure-feedback system into a system with strict-feedback structure,so that the well-known backstepping method can be ***,in the backstepping design process,the BLFs are employed to avoid the violation of the state constraints,and neural networks(NNs)are directly used to online approximate the unknown packaged nonlinear *** presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to ***,it is shown that the constraint requirement on the system will not be violated during the ***,two simulation examples are provided to show the effectiveness of the proposed control scheme.

关键词： Asymptotic tracking control barrier Lyapunov functions full state constraints nonlinear pure-feedback systems

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Global Navigation Satellite System Reflectometry specular point positioning method based on reflection geometric model: Suitable for land and ocean applications

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Li, Shuaishuai He, Shanbao Cheng, Xing Wang, Feng Xie, Heng Pang, Jingjing Cai, Min Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing China Beihang University School of Electronic and Information Engineering Beijing China Beijing Information Science and Technology University School of Information and Communication Engineering Beijing China

The accurate positioning of the specular point of the spaceborne global navigation satellite system reflectometry (GNSS-R) receiver is a key step in using GNSS-R technology for geophysical parameter retrieval. As GNSS-R applications expand across diverse environments ranging from land and oceans to glaciers, traditional geometric positioning accuracy is no longer sufficient to meet the demands of these varied applications. Specifically, positioning algorithms must not only facilitate real-time spaceborne processing but also adapt to fluctuating surface conditions, all while sustaining high levels of accuracy and efficiency. In response to these demands, we propose a positioning algorithm tailored for spaceborne GNSS-R specular points across various surface environments. The proposed algorithm integrates actual surface height data by leveraging surface models for localized terrain fitting. It employs a regional grid traversal method, guided by geographic positioning criteria, to accurately search for specular points that satisfy specific conditions. Considering the impact of initial position accuracy on traversal speed, we developed an ellipsoidal reflection geometric model based on the unique properties of the ellipsoid. This model facilitates the swift, non-iterative determination of a high-precision initial position for specular points. Experimental data indicate that our proposed approach reduces the Euclidean distance error of the initial position by ~87.5% and enhances grid traversal efficiency by a factor of more than eight times. To verify the robustness of the proposed method, this paper conducted multiple experiments in both ocean and land scenarios, including case studies in the Qinghai-Tibet Plateau region. The findings highlight that the normal vector angle analysis criterion offers superior reliability in complex terrains, positioning it as the optimal criterion for the positioning of specular points. © 2025 Society of Photo-Optical Instrumentation Engi

关键词： Global positioning system

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A Multi-Agent Proximal Policy Optimized Joint Mechanism in mmWave HetNets with CoMP Toward Energy Efficiency Maximization

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第1期8卷 265-278页

作者： Lotfolahi, Amin Ferng, Huei-Wen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106319 Taiwan

A novel cluster-based traffic offloading and user association (UA) algorithm alongside a multi-agent deep reinforcement learning (DRL) based base station (BS) activation mechanism is proposed in this paper. Our design aims to maximize the energy efficiency (EE) of the heterogeneous network (HetNet) while maintaining high quality of service (QoS). By taking advantage of the dense deployment of BSs in a HetNet, a clustering algorithm is first proposed to facilitate traffic offloading among BSs. Then, a multi-agent proximal policy optimization (MAPPO) based DRL algorithm is employed to trigger the BS activation decision based on the current environmental condition. Finally, a UA algorithm is deployed to improve further the (normalized) data rate of all users, known as the (normalized) sum rate. Via simulation, we show that our proposed mechanism can remarkably enhance the EE and excel over the closely related mechanisms. It satisfies the required data rate, improves the sum rate, and exhibits excellent scalability when many BSs are deployed. © 2017 IEEE.

关键词： Quality of service

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A topical VAEGAN-IHMM approach for automatic story segmentation

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Mathematical Biosciences and engineering 2024年第7期21卷 6608-6630页

作者： Yu, Jia Peng, Huiling Wang, Guoqiang Shi, Nianfeng School of Computer and Information Engineering Luoyang Institute of Science and Technology China Software Research Institute Technological University of Shannon Ireland

Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance. © 2024 the Author(s).

关键词： Hidden Markov models

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Hyperspectral Target Detection-Based 2-D-3-D Parallel Convolutional Neural Networks for Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 9451-9469页

作者： Chen, Shih-Yu Hsu, Kai-Hsun Kuo, Tzu-Hsien National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin64002 Taiwan

This article presents a novel hyperspectral target detection (HTD) based two-dimensional (2-D)-three-dimensional (3-D) parallel convolutional neural network (HTD 2D-3D-PCNN) model, which integrates the HTD technique to achieve outstanding performance in hyperspectral image classification. The proposed model effectively leverages both spectral and spatial information through a dual-branch architecture in hyperspectral imaging. In the first branch, HTD is utilized to enhance the spectral features of targets of interest, while suppressing background. The resulting enhanced image is then inputted to a 2D-CNN, augmented with an additional deconvolution layer to highlight spatial characteristics. Concurrently, the second branch employs dimensionality reduction via principal component analysis, and a 3D-CNN is employed to capture both spectral and spatial attributes. Subsequently, the feature maps from both convolutional neural networks (CNNs) are combined and processed through fully connected layers for classification. To validate the effectiveness of the proposed HTD-2D-3D-PCNN, extensive experiments are conducted on five widely used hyperspectral public datasets (Indian Pines, Pavia University, Salinas Scene, Kennedy Space Center, and Botswana) with a consistent training sample ratio of either 10% or 5%. The results show that HTD-2D-3D-PCNN can achieve overall accuracy values of 98.41%, 99.85%, 99.92%, 99.82%, and 98.82% for the respective datasets, surpassing the performance of recent methodologies. © 2008-2012 IEEE.

关键词： Image classification

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Introduction

Signals and Communication Technology

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Signals and Communication technology 2024年 Part F1492卷 3-17页

作者： Tian, Yu-Chu Gao, Jing School of Computer Science Queensland University of Technology BrisbaneQLD Australia College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

The main theme of this book is network analysis and architecture design for network planning. While there are various types of networks, this book specifically focuses on computer networks for data communication. Therefore, it uses the term networks to specifically refer to computer networks unless specified explicitly otherwise. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Real-time all-frequency global llumination with radiance caching

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Computational Visual Media 2024年第5期10卷 923-936页

作者： Youxin Xing Gaole Pan Xiang Chen Ji Wu Lu Wang Beibei Wang School of Software Shandong UniversityJinan 250101China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Global illumination(GI)plays a crucial role in rendering realistic results for virtual exhibitions,such as virtual car *** scenarios usually include all-frequency bidirectional reflectance distribution functions(BRDFs),although their geometries and light configurations may be *** allfrequency BRDFs in real time remains challenging due to the complex light *** approaches,including precomputed radiance transfer,light probes,and the most recent path-tracing-based approaches(ReSTIR PT),cannot satisfy both quality and performance requirements ***,we propose a practical hybrid global illumination approach that combines ray tracing and cached GI by caching the incoming radiance with *** approach can produce results close to those of ofline renderers at the cost of only approximately 17 ms at runtime and is robust over all-frequency *** approach is designed for applications involving static lighting and geometries,such as virtual exhibitions.

关键词： real-time global illumination all-frequency BRDFs Haar wavelets radiance caching

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Vision-based Robotic Arm in Defect Detection and Object Classification Applications

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Sensors and Materials 2024年第2期36卷 655-670页

作者： Lin, Cheng-Jian Jhang, Jyun-Yu Gao, Yi-Jyun Huang, Hsiu-Mei Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404 Taiwan Department of Information Management National Taichung University of Science and Technology Taichung404 Taiwan

Robotic arms have been widely used in industrial fields. However, researchers have seldom considered the factors affecting the actual factory environment. For example, when objects are conveyed in a factory, conveyor belts are often used to dynamically plan the overall production line. In addition, each object requires multiple checkpoints for repeated audits and inspections to ensure its quality. In this study, a vision-based robotic arm system equipped with multiple functionalities was developed. The development process consisted of three steps: detecting multiple dynamic objects, determining the size of each object, and identifying object defects. In the first step, You Only Look Once was used to detect multiple dynamic objects on a conveyor belt in real time. In the second step, the original image of the object was converted into a grayscale image, and the edge contour of the object was drawn using a Canny edge detection algorithm. Objects in the image were then rotated for vertical and horizontal projections, and then an artificial neural network (ANN) was used to calculate the size of each object. In the third step, a convolutional fuzzy neural network (CFNN) was used to identify object defects. This network was divided into an input layer, a convolution pooling layer, a feature fusion layer, a fuzzy layer, a regular layer, and a defuzzification layer. According to the experimental results, the standard error of the mean between the object size obtained by the ANN and the actual size was 0.009. In addition, the accuracy, recall, precision, and F1-score obtained by the CFNN in object defect detection were 0.9580, 0.9535, 0.9535, and 0.9535, respectively. Compared with other deep neural network models, such as AlexNet and LeNet, the proposed CFNN has fewer parameters and higher performance. © MYU K.K.

关键词： Fuzzy neural networks

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Task Offloading and Resource Management for IIoT With Satellite–Terrestrial Integrated Computing Power Network Based on D3QN

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IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies, such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT;2) the complex communication environments;and 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated CPN (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a markov decision process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2014 IEEE.

关键词： Energy utilization

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Data-driven output regulation control for constrained linear systems

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science China(information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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