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Control Policy Learning Design for Vehicle Urban Positioning via BeiDou Navigation

Journal of Systems Science & Complexity 2024年第1期37卷 114-135页

作者： QIN Yahang ZHANG Chengye CHEN Ci xie shengli LEWIS Frank L School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China. Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China. UTA Research Institute The University of Texas at ArlingtonFort WorthTX 76019USA

This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via BeiDou *** navigating in urban canyons,the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the BeiDou ***,the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system,and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback *** proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system,without the full knowledge of the vehicle *** is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate.A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of BeiDou navigation in the urban environment.

关键词： BeiDou navigation multipath effect prescribed convergence rate reinforcement learning urban localization.

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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 430-445页

作者： Jiawen Kang Junlong Chen Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong shengli xie IEEE School of Automation Guangdong University of TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing based on IoT Technology Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Pillar of Information Systems Technology and Design Singapore University of Technology and DesignSingaporeSingapore College of Communication Engineering Army Engineering University of PLANanjing 210007China National Natural Science Foundation of China Beijing 100085China Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach

关键词： Avatar blockchain metaverses multi-agent deep reinforcement learning transformer UAVs

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Learning the continuous-time optimal decision law from discrete-time rewards

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National Science Open 2024年第5期3卷 130-147页

作者： Ci Chen Lihua xie Kan xie Frank Leroy Lewis Yilu Liu shengli xie School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education School of Electrical and Electronic Engineering Nanyang Technological University 111 Center for Intelligent Batch Manufacturing Based on IoT Technology UTA Research Institute the University of Texas at Arlington Department of Electrical Engineering and Computer Science University of Tennessee Oak Ridge National Laboratory Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing

The concept of reward is fundamental in reinforcement learning with a wide range of applications in natural and social sciences. Seeking an interpretable reward for decision-making that largely shapes the system's behavior has always been a challenge in reinforcement learning. In this work, we explore a discrete-time reward for reinforcement learning in continuous time and action spaces that represent many phenomena captured by applying physical laws. We find that the discrete-time reward leads to the extraction of the unique continuous-time decision law and improved computational efficiency by dropping the integrator operator that appears in classical results with integral rewards. We apply this finding to solve output-feedback design problems in power systems. The results reveal that our approach removes an intermediate stage of identifying dynamical models. Our work suggests that the discrete-time reward is efficient in search of the desired decision law, which provides a computational tool to understand and modify the behavior of large-scale engineering systems using the optimal learned decision.

关键词： continuous-time state and action decision law learning discrete-time reward dynamical systems reinforcement learning

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交互与沉浸:元宇宙赋能乡愁旅游的空间转换与业态创新

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昆明理工大学学报（社会科学版） 2024年第1期24卷 147-154页

作者：解胜利安徽大学社会与政治学院安徽合肥230601

作为对IT技术的综合集成运用,元宇宙将促进现实世界和虚拟世界的联接与融合,提升人类实践活动的交互性和沉浸感。乡愁旅游是乡村旅游的新形式,元宇宙在打破时空限制、进行技术赋能方面的优势为乡愁旅游空间的重构和业态创新提供了新的... 详细信息

作为对IT技术的综合集成运用,元宇宙将促进现实世界和虚拟世界的联接与融合,提升人类实践活动的交互性和沉浸感。乡愁旅游是乡村旅游的新形式,元宇宙在打破时空限制、进行技术赋能方面的优势为乡愁旅游空间的重构和业态创新提供了新的可能。探索利用元宇宙的AR、VR、MR等技术通过以虚补实、无中生有、实幻交织的方式可以打造出多层次和全时空的乡愁旅游空间,元宇宙也有助于推进乡愁旅游融合化、游戏化和社交化的业态创新,形成更具交互性和沉浸感的乡愁旅游产品。元宇宙与乡愁旅游业的结合,不仅可以推动旅游业本身的转型升级,也将助力乡村人才回流,非遗传承发展,集体经济模式创新和产业融合发展。因此,需要加强顶层设计,加快数字乡村建设,开发出更能满足人们乡愁情感的文旅产品,以文旅产业兴旺助推乡村振兴。

关键词：元宇宙乡愁旅游沉浸式体验数字乡村乡村振兴

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考虑信息年龄的无人机辅助智能交通系统计算卸载优化

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电子与信息学报 2024年第3期46卷 934-943页

作者：钟伟锋黄旭民康嘉文谢胜利广东工业大学自动化学院广州510006

该文考虑无人机(UAV)交通监测与移动边缘计算(MEC)技术结合的智能交通系统。为了保障系统中数据时效性并且降低系统能耗,提出计及信息年龄(AoI)的UAV计算卸载优化方法。首先,建立UAV辅助的MEC系统模型,允许MEC服务器缓存常用的应用程序... 详细信息

该文考虑无人机(UAV)交通监测与移动边缘计算(MEC)技术结合的智能交通系统。为了保障系统中数据时效性并且降低系统能耗,提出计及信息年龄(AoI)的UAV计算卸载优化方法。首先,建立UAV辅助的MEC系统模型,允许MEC服务器缓存常用的应用程序并为UAV提供计算卸载,以支持UAV执行交通监测任务。通过联合优化UAV任务卸载决策、UAV上下行通信带宽分配以及被卸载任务的计算资源分配,最小化所有UAV与MEC服务器的总能耗,同时满足AoI与资源容量等约束条件。其次,系统能耗最小化问题是混合整数非凸优化问题,因此采用离散化和线性化手段,快速获得问题的近似最优解,并设计离散点生成算法来调节近似误差。最后,仿真结果表明,即使对于大型的非凸问题,所提方法也能够快速得到近似最优解,并且可以在不同的任务场景中满足AoI等约束条件,最大限度降低系统能耗。仿真结果验证了所提方法的有效性。

关键词：移动边缘计算智能交通系统无人机信息年龄能耗优化

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相衬光学相干弹性成像的超分辨应变测量

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光学精密工程 2024年第12期32卷 1812-1823页

作者：张展华曹鑫詹伟浩董博谢胜利白玉磊广东工业大学自动化学院广东广州510006

相衬光学相干弹性成像的相位分辨率受限于系统的光源带宽,导致层析应变成像质量低。这严重制约了相衬光学相干弹性成像的实际应用与发展。为此,本文提出一种基于数据驱动的超分辨应变测量方法,以解决相位分辨率受限下的应变重构困难问... 详细信息

相衬光学相干弹性成像的相位分辨率受限于系统的光源带宽,导致层析应变成像质量低。这严重制约了相衬光学相干弹性成像的实际应用与发展。为此,本文提出一种基于数据驱动的超分辨应变测量方法,以解决相位分辨率受限下的应变重构困难问题。首先,根据相衬光学相干弹性成像原理,搭建了层析应变测量模型,用于获取网络训练所需的数据集,解决实际测量过程中超分辨应变标签难以获取的问题。其次,构建深度卷积神经网络,通过带有层析分辨率受限特点的数据驱动方式,让网络学习低分辨相位与高分辨应变之间的映射关系,从而实现相衬光学相干弹性成像的超分辨应变测量。最后,采用数值验证和压缩变形加载实验对本文方法的性能进行验证。实验结果表明本文方法在窄带宽输出下能重构出宽带宽的应变测量效果,并且其信噪比相比于矢量方法和传统应变计算深度神经网络,分别提高了18.4 dB和1.45 dB。本文方法可以突破系统光源的带宽限制,在低分辨相位输入条件下实现超分辨应变测量,从而加快相衬光学相干弹性成像在力学性能表征、内部早期损伤探测等方面的应用前景。

关键词：光学弹性成像相衬技术应变测量深度神经网络超分辨重构

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多元酯化淀粉在新型纤维品种中的上浆工艺

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天津纺织科技 2024年第2期 46-48页

作者：解胜利李蕾石家庄常山北明科技股份有限公司恒盛纺织分公司河北石家庄050000

探讨多元酯化淀粉JF-999的浆料性能,替代PVA用于新型纤维纱线上浆的效果。对比测试JF-999浆料和常规PVA浆料的浆液和浆纱性能,对两种配方的浆纱各项指标分析,以及织造生产效率对比。试验结果表明:JF-999多元酯化淀粉黏度及稳定性较好,... 详细信息

探讨多元酯化淀粉JF-999的浆料性能,替代PVA用于新型纤维纱线上浆的效果。对比测试JF-999浆料和常规PVA浆料的浆液和浆纱性能,对两种配方的浆纱各项指标分析,以及织造生产效率对比。试验结果表明:JF-999多元酯化淀粉黏度及稳定性较好,与天竹等新型纤维具有良好的亲和性,且黏着力较好;浆纱质量稳定,好轴率较高,具有良好的浆纱增强和减伸效果,布机效率提升明显;易退浆、无污染,完全符合绿色环保要求,可以替代PVA用于新型纤维品种的经纱上浆。

关键词： JF-999多元酯化淀粉黏度稳定性环保上浆新型纤维布机效率

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Construction of compact RBF network by refining coarse clusters and widths

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Journal of Systems Engineering and Electronics 2009年第6期20卷 1309-1315页

作者： Zeng Delu Zhou Zhiheng xie shengli School of Electronic and Information Engineering South China Univ. of Technology Guangzhou 510641 P. R. China

It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function（RBF） *** paper aims at constructing a compact RBF network with two main *** the first step, the coarse clusters computed from triangle inequalities are refined to obtain the locations of centers by the defined maximum degree spanning tree（MDST）.Meanwhile the coarse widths are *** the second step, a learning algorithm referred to as anisotropic gradient descent method is presented to further refine the above coarse *** of the proposed algorithm show its great performance in times series prediction and classification.

关键词： clustering anisotropic gradient descent radial basis function time series prediction boundary extraction.

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Global minimization of adaptive local image fitting energy for image segmentation

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Journal of Systems Engineering and Electronics 2014年第2期25卷 307-313页

作者： Guoqi Liu Zhiheng Zhou shengli xie School of Electronic and Information Engineering South China University of Technology School of Automation Guangdong University of Technology

The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better per- formance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the bound- aries, and a global minimization of the active contour model is presented, in addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomo- geneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.

关键词： image segmentation level set adaptive local imagefitting (LIF) energy.

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Fast nonnegative tensor ring decomposition based on the modulus method and low-rank approximation

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Science China(Technological Sciences) 2021年第9期64卷 1843-1853页

作者： YU YuYuan xie Kan YU JinShi JIANG Qi xie shengli School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong-Hong Kong-Macao Joint Laboratory for Smart Manufacturing Guangzhou 510006China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangdong University of TechnologyGuangzhou 510006China

Nonnegative tensor ring(NTR) decomposition is a powerful tool for capturing the significant features of tensor objects while preserving the multi-linear structure of tensor data. The existing algorithms rely on frequent reshaping and permutation operations in the optimization process and use a shrinking step size or projection techniques to ensure core tensor nonnegativity, which leads to a slow convergence rate, especially for large-scale problems. In this paper, we first propose an NTR algorithm based on the modulus method(NTR-MM), which constrains core tensor nonnegativity by modulus transformation. Second, a low-rank approximation(LRA) is introduced to NTR-MM(named LRA-NTR-MM), which not only reduces the computational complexity of NTR-MM significantly but also suppresses the noise. The simulation results demonstrate that the proposed LRA-NTR-MM algorithm achieves higher computational efficiency than the state-of-the-art algorithms while preserving the effectiveness of feature extraction.

关键词： nonnegative tensor ring decomposition modulus method low-rank approximation

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