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A cooperative on-ramp merging strategy based on the hybrid of centralized and distributed interaction for heterogeneous vehicles

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Journal of Ambient Intelligence and Humanized computing 2023年第3期14卷 2385-2397页

作者： Wang, ShiHui Zhao, Min Sun, DiHua Liu, ZhongCheng Key Laboratory of Dependable Service Computing in the Cyber Physical Society of the Ministry of Education and School of Automation Chongqing University Chongqing China

The on-ramp merging on the freeway which causes reduced traffic efficiency, increased risks of collision, and more fuel consumption is one of the research hotspots. The mixed traffic made up of connected and autonomous vehicles (CAVs) and connected and human-driving vehicles (CHVs) is the trend of the future intelligent traffic. Therefore, to facilitate the on-ramp merging on the freeway in mixed traffic, this paper proposes a hybrid cooperative strategy to the heterogeneous vehicles in a merging zone. As a cyber-physical system, the on-ramp merging on the freeway in mixed traffic was divided into four types and analyzed. In this study, we proposed a synergetic merging framework based on the vehicle-infrastructure cooperative theory for the on-ramp merging to ensure successful merging of mixed traffic. For different types of vehicles in mixed traffic, we designed different control methods, respectively. For CHVs and CAVs, centralized induced control method (CICM) and distributed autonomous control method (DACM) were designed. When designing these two methods, we separately considered two classic and important human factors: the reaction time of drivers of CHVs and the comfort of CAVs’ passengers. Finally, the effectiveness of the proposed strategy and methods was validated through simulations. The proposed hybrid cooperative strategy can derive an optimal trajectory for each vehicle. The control method of CAVs can obtain the optimal trajectory while satisfy the comfort requirements of passengers. By analyzing results of simulations, it can be known that the reaction time of drivers has a certain effect on the state of the CHVs’ merging process. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： cyber physical System

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Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 219-230页

作者： Dahui Luo Yujuan Wang Yongduan Song the Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqing UniversityChongqing 400044 the School of Automation Chongqing UniversityChongqing 400044 the Star Institute for Intelligent Systems Chongqing 400044 IEEE

This paper investigates the prescribed-time control(PTC) problem for a class of strict-feedback systems subject to non-vanishing uncertainties. The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC synthesis nontrivial. In this work, a control method that does not involve infinite time-varying gain is proposed, leading to a practical and global prescribed time tracking control solution for the strict-feedback systems, in spite of both the mismatched and nonvanishing uncertainties. Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point, in our method a softening unit is exclusively included to ensure the continuity of the control action. Furthermore, in contrast to most existing prescribed-time control works where the control scheme is only valid on a finite time interval, in this work, the proposed control scheme is valid on the entire time interval. In addition, the prior information on the upper or lower bound of gi is not in need,enlarging the applicability of the proposed method. Both the theoretical analysis and numerical simulation confirm the effectiveness of the proposed control algorithm.

关键词： Adaptive control prescribed time control(PTC) strict-feedback systems tracking control

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Dynamic QoS Mapping and Adaptive Semi-Persistent Scheduling in 5G-TSN Integrated Networks

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China Communications 2023年第4期20卷 340-355页

作者： Yueping Cai Xiaowen Zhang Shaoliu Hu Xiaocong Wei School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400030China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing UniversityChongqing 400030China

The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids.5G-TSN(Time-Sensitive Networking)integrated networks with the 5G system(5GS)as a TSN bridge are promising to provide the required communication *** guarantee the endto-end(E2E)QoS(Quality of service)performance of traffic is a great challenge in 5G-TSN integrated networks.A dynamic QoS mapping method is proposed in this *** is based on the improved K-means clustering algorithm and the rough set theory(IKCRQM).The IKC-RQM designs a dynamic and loadaware QoS mapping algorithm to improve its *** adaptive semi-persistent scheduling(ASPS)mechanism is proposed to solve the challenging deterministic scheduling in *** includes two parts:one part is the persistent resource allocation for timesensitive flows,and the other part is the dynamic resource allocation based on the max-min fair share *** results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping,and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.

关键词： 5G-TSN integrated networks QoS mapping traffic scheduling resource allocation

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Modeling and Simulation of Mixed Traffic in Tunnel Entrance Area Based on Social Force and Artificial Potential field 24

Modeling and Simulation of Mixed Traffic in Tunnel Entrance ...

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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Chai, Jihao Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education School of Automation Chongqing University Chongqing Chongqing400044 China

ISBN: (纸本)9798400716942

The traffic environment in the tunnel entrance area is quite complex, representing a typical road bottleneck. Therefore, it is necessary to propose corresponding control measures to improve the traffic efficiency in this area. This paper, based on the social force model and artificial potential field model, establishes a dynamic model for human-driven vehicles (HVs) in the tunnel entrance area. Subsequently, a dynamic model for connected and autonomous vehicles (CAVs) is designed, and a hybrid traffic model for the tunnel entrance area is developed. Simulation experiments indicate that the unique environment at the tunnel entrance area can lead to congestion, and increasing the penetration rate of CAVs can effectively enhance the traffic efficiency in this region. © 2024 ACM.

关键词： Digital elevation model

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RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1640-1642页

作者： Qiwu Zhu Qingyu Xiong Zhengyi Yang Yang Yu School of Big Data&Software Engineering Chongqing UniversityChongqing 400044 Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqing 400044China School of Biology and Engineering Guizhou Medical UniversityGuiyang 550025China

Dear Editor,This letter focuses on the problem of remaining useful life(RUL)prediction of equipment. Existing graph neural network(GCN)-based approaches merely provide the point estimation of RUL. However,the estimated RUL often varies widely due to the model parameters and the noise in data. It is important to know the uncertainty in predictions for reliable risk analysis and maintenance decision *** map the relationship between noisy condition monitoring data and RUL with uncertainty.

关键词： letter estimation prediction

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A Feature Distribution Smoothing Network Based on Gaussian Distribution for QoS Prediction

A Feature Distribution Smoothing Network Based on Gaussian D...

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2023 IEEE International Conference on Web services, ICWS 2023

作者： Lu, Tongxin Zhang, Xiaohong Wang, Ziliang Yan, Meng Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China Chongqing University School of Big Data and Software Engineering Chongqing China

ISBN: (纸本)9798350304855

With the increasing number of services and their homogenization, the use of Quality of service (QoS) for recommendations has become necessary. However, existing QoS prediction solutions have limitations in solving the noise and label imbalance problems of dataset, which greatly limit the improvement of QoS prediction accuracy. In this paper, we propose FSNet that contains a feature distribution smoothing module and an improved W-Huber loss function. The feature distribution smoothing module mitigates the effect of noise problem by fitting potential Gaussian distribution of known features with a supervised feedforward neural network. W-Huber loss function mitigates the impact of label imbalance problem on QoS prediction by reweighting the two components of Huber loss function. We conduct extensive experiments on real large-scale QoS dataset, and the results demonstrate that the proposed FSNet method outperforms existing QoS prediction methods. © 2023 IEEE.

关键词： Forecasting

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Adaptive Hybrid FFT: A Novel Pipeline and Memory-Based Architecture for Radix-2k FFT in Large Size Processing 22

Adaptive Hybrid FFT: A Novel Pipeline and Memory-Based Archi...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhao, Fangyu Xiao, Chunhua Wang, Zhiguo Du, Xiaohua Dong, Bo Chongqing University College of Computer Science Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society China Sichuan Huacun Zhigu Technology Co. Ltd. Chengdu China

ISBN: (纸本)9798331509712

In the field of digital signal processing, the fast Fourier transform (FFT) is a fundamental algorithm, with its processors being implemented using either the pipelined architecture, well-known for high-throughput applications but weak in hardware utilization, or the memory-based architecture, designed for area-constrained scenarios but failing to meet stringent throughput requirements. Therefore, we propose an adaptive hybrid FFT, which leverages the strengths of both pipelined and memory-based architectures. In this paper, we propose an adaptive hybrid FFT processor that combines the advantages of both architectures, and it has the following features. First, a set of radix-2kmulti-path delay commutators (MDC) units are developed to support high-performance large-size processing. Second, a conflict-free memory access scheme is formulated to ensure a continuous data flow without data contention. Third, We demonstrate the existence of a series of bit-dimension permutations for reordering input data, satisfying the generalized constraints of variable-length, high-radix, and any level of parallelism for wide adaptivity. Furthermore, the proposed FFT processor has been implemented on a field-programmable gate array (FPGA). As a result, the proposed work outperforms conventional memory-based FFT processors by requiring fewer computation cycles. It achieves higher hardware utilization than pipelined FFT architectures, making it suitable for highly demanding applications. © 2024 IEEE.

关键词： Fast Fourier transforms

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP Architecture 22

Optimizing Batched Small Matrix Multiplication on Multi-core...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zuo, Xiaohan Xiao, Chunhua Wang, Qinglin Shi, Chen Chongqing University College of Computer Science Chongqing China Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha410073 China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Changsha410073 China

ISBN: (纸本)9798331509712

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many High-Performance computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x. © 2024 IEEE.

关键词： Network security

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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