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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Xiaolin Ma Yi Peng Qingqing Yang College of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Science Kunming University of Science and Technology Kunming China

Aiming at the problem that the total throughput of secondary users is not maximized, considering the fairness between secondary users and the interference of primary users to secondary users, a power allocation strategy to maximize the throughput of CR-NOMA is proposed. Firstly, the elementary matrix is established according to the equivalent channel gain, and the secondary user is selected and multiplexed to the primary user sub-channel. Then, for the selected secondary users, the minimum transmission rate is dynamically adjusted based on fairness, and the optimal power scaling factor under the maximization of the total throughput of the secondary users is calculated. Finally, power distribution is performed. The simulation results show that the proposed algorithm can effectively reduce the complexity of the algorithm and maximize the total throughput of the secondary users compared with the HPPA and CR-OMA algorithms.

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

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IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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ADAS Evolution: LSTM-Driven Autonomous Adaptation to Individual Driver Preferences

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IFAC-PapersOnLine 2024年第30期58卷 230-235页

作者： Y. Wang N. Ota S. Debernard J-C. Popieul Univ. Polytechnique Hauts-de-France CNRS UMR 8201 – LAMIH - Laboratory of Industrial and Human Automation Control Mechanical engineering and Computer science F-59313 Valenciennes France University of Tsukuba Tsukuba Japon

The ongoing advancement of advanced driver assistance systems (ADAS) has led to notable improvements in vehicle safety and efficiency within the automotive industry (Kala, 2016). Nevertheless, these systems also present new challenges and demands for developers, especially concerning driver trust and acceptance, which are vital for the safety and efficiency of collaborative human-machine driving. In response to these problems, the CoCoVéIA project proposes an innovative approach in which the driver assistance system (ADAS) integrates continuous learning of individual driving preferences, to boost driver confidence and acceptance. At the core of this method is the use of autonomous learning algorithms, particularly long short-term memory (LSTM) networks, which are adept at learning and adjusting to different driving preferences in real-time. This paper confirms the efficacy of LSTM algorithms in precisely learning and predicting driving preferences through experimental data, showcasing their ability to accommodate a variety of driver behaviors and preferences.

关键词： ADAS Intelligent Vehicles Machine Learning LSTM Human-Machine Cooperation

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Fusion enhancement : UAV target detection based on multi-modal GAN

Fusion enhancement : UAV target detection based on multi-mod...

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Rui Li Yi Peng Qingqing Yang College of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Science Kunming University of Science and Technology Kunming China

UAV target detection has important application value in the fields of modern military, security and emergency rescue. In order to improve the accuracy and robustness of UAV target detection, this paper uses image fusion technology to enhance the information of input images, and combines YOLOv5 target detection model to introduce attention mechanism for improvement. In the experiment, the generative adversarial network (GAN) is used as the image fusion technology, and the fusion image is generated by changing the structure of the generator and the discriminator. At the same time, the attention mechanism is introduced into the YOLOv5 model to enhance the focus of target detection. The experimental results show that the proposed improved algorithm has achieved significant performance improvement in the UAV target detection task.

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ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence

arXiv

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arXiv 2024年

作者： Mei, Wenjie Zheng, Dongzhe Li, Shihua The School of Automation The Key Laboratory of MCCSE The Ministry of Education Southeast University Nanjing China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Neural ODEs (NODEs) are continuous-time neural networks (NNs) that can process data without the limitation of time intervals. They have advantages in learning and understanding the evolution of complex real dynamics. Many previous works have focused on NODEs in concise forms, while numerous physical systems taking straightforward forms, in fact, belong to their more complex quasi-classes, thus appealing to a class of general NODEs with high scalability and flexibility to model those systems. This, however, may result in intricate nonlinear properties. In this paper, we introduce ControlSynth Neural ODEs (CSODEs). We show that despite their highly nonlinear nature, convergence can be guaranteed via tractable linear inequalities. In the composition of CSODEs, we introduce an extra control term for learning the potential simultaneous capture of dynamics at different scales, which could be particularly useful for partial differential equation-formulated systems. Finally, we compare several representative NNs with CSODEs on important physical dynamics under the inductive biases of CSODEs, and illustrate that CSODEs have better learning and predictive abilities in these settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Performance analysis of STAR-RIS-assisted NOMA communication

Performance analysis of STAR-RIS-assisted NOMA communication

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Jian Tang Yi Peng Qingqing Yang College of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Science Kunming University of Science and Technology Kunming China

This paper analyzes the outage probability of a non-orthogonal multiple access (NOMA) system assisted by a reconfigurable smart surface (STAR-RIS) with simultaneous emission and reflection. Among them, the link between the base station and the user is a non-line-of-sight link, which requires relay communication through STAR-RIS. We assume that the channel is a Rayleigh channel, and derive the closed-form expressions of the outage probability of the reflected and refracted users in the NOMA system. The numerical results show that: 1) the outage probability decreases with the increase of the reflective element of STAR-RIS; 2) the outage probability decreases with the decrease of the deployment range of STAR-RIS; 3) increasing the power allocation factor can effectively reduce the outage probability of users; 4) the average energy efficiency of reflective users is inversely proportional to that of refractive users.

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A Decomposition-Based Evolutionary Algorithm With Clustering and Hierarchical Estimation for Multi-Objective Fuzzy Flexible Jobshop Scheduling

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, Xuwei Liu, Shixin Zhao, Ziyan Yang, Shengxiang State Key Laboratory of Synthetical Automation for Process Industries and College of Information Science and Engineering Northeastern University Shenyang China School of Computer Science and Informatics Institute of Artificial Intelligence De Montfort University Leicester U.K

As an effective approximation algorithm for multi-objective jobshop scheduling, multi-objective evolutionary algorithms (MOEAs) have received extensive attention. However, maintaining a balance between the diversity and convergence of non-dominated solutions while ensuring overall convergence is an open problem in the context of solving Multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs). To address it, we propose a new MOEA named MOEA/DCH by introducing a hierarchical estimation method, a clustering-based adaptive decomposition strategy, and a heuristic-based initialization method into a basic MOEA based on decomposition. Specifically, a hierarchical estimation method balances the convergence and diversity of non-dominant solutions by integrating Pareto dominance and scalarization function information. A clustering-based adaptive decomposition strategy is constructed to enhance the population’s ability to approximate a complex Pareto front. A heuristic-based initialization method is developed to provide high-quality initial solutions. The performance of MOEA/DCH is verified and compared with five competitive MOEAs on widely-tested benchmark datasets. Empirical results demonstrate the effectiveness of MOEA/DCH in balancing the diversity and convergence of non-dominated solutions while ensuring overall convergence. IEEE

关键词： Job shop scheduling

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PQMPV: Parallel and Quantified Dense Stereo Disparity Estimation Based on Multi-Path Viterbi 8

PQMPV: Parallel and Quantified Dense Stereo Disparity Estima...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Gu, Rongqi Wang, Tianhang Zou, Tianpei Zhang, Bo Li, Zhijun Zhang, Haotian Chen, Guang Tongji University Department of Automotive Engineering Shanghai China Westwell-lab Shanghai China Tongji-Westwell Driverless Commercial Joint Laboratory Shanghai China Tongji University Department of Computer Science and Technology Shanghai China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China University of Science and Technology of China Department of Automation Hefei China

ISBN: (纸本)9798350300178

Stereo depth estimation plays a crucial role in enabling robots, autonomous driving and other applications. As an ill-posed problem, both deep learning based and traditional methods have been extensively studied in recent years. Traditional methods mainly focus on feature matching between multiple images to estimate depth, but often result in blank pixels with zero disparity and also require significant computation time. In this paper, we propose a new stereo disparity estimation algorithm based on the dynamic programming method known as Viterbi. Our approach optimize the range of disparity calculation through image pyramid, combined with Viterbi algorithm for cost aggregation, and accelerates several calculation steps through parallel computing techniques. Additionally, we improve data transmission and processing speed by quantifying data from float to integer. Experiments on the KITTI dataset and MiddEval benchmark demonstrate that our algorithm achieves significant improvements in average error rate and processing speed of disparity map, making it a promising solution for stereo depth estimation in practical applications. © 2023 IEEE.

关键词： Benchmarking

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Spectral-Spatial Fusion Hypergraph Convolutional Network for Multispectral Point Cloud Classification 11

Spectral-Spatial Fusion Hypergraph Convolutional Network for...

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11th International Conference on Information Systems and Computing Technology, ISCTech 2023

作者： Huang, Jiangbo Wang, Qingwang Zhang, Zifeng Liu, Sivang Wang, Zhifeng Shen, Tao Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Kunming China Kunming University of Science and Technology School of Information Engineering and Automation Kunming China Real Estate Resource Management Center General Logistics Department of Pla Beijing China Beijing Anlu International Technology Co Beijing China

ISBN: (纸本)9798350342406

Graph Neural Networks (GNNs) based on simple graph can directly model 3D multispectral point clouds, thus avoiding the problem of 3D spatial information loss when converting 3D point clouds to 2D feature images. However, considering the dense distribution of objects in remote sensing scenes, GNNs only consider the association between pairs of vertices, which is insufficient for exploring high-order correlations and long-term dependence correlations. Hypergraph neural network (HGNN) is a more generalized framework that overcomes the limitations of traditional simple graph. In this paper, we propose a spectral-spatial fusion hypergraph convolutional network (S2FHGCN) for 3D multispectral point cloud classification. S2FHGCN first generates two hypergraphs from multidimensional features including spectral feature and spatial feature, respectively. The hypergraph structure can better handle complex high-order correlations for representation learning. Then the two hypergraphs are fused to obtain global representation information. Finally, the multidimensional features and the fused hypergraph are input to the hypergraph convolution network. The experimental results on real multispectral point clouds demonstrate the effectiveness of our proposed S2FHGCN for multispectral point cloud classification. © 2023 IEEE.

关键词： Remote sensing

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Novel Path Following for a Four-Wheel Steering Vehicle Based on Model Predictive Control 8

Novel Path Following for a Four-Wheel Steering Vehicle Based...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Gu, Rongqi Wang, Tianhang Zhang, Bo Li, Zhijun Li, Tianhao Chen, Guang Tongji University Department of Automotive Engineering Shanghai China Westwell-lab Shanghai China Tongji-Westwell Driverless Commercial Joint Laboratory Shanghai China Department of Computer Science and Technology Tongji University Shanghai China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China University of Science and Technology of China Department of Automation Hefei China

ISBN: (纸本)9798350300178

Path following is a crucial technique for ensuring the safe and efficient operation of automatic electric vehicles. Four-wheel steering (4WS) technology is known to enhance the accuracy and flexibility of such vehicles. In this paper, we propose a new constrained model predictive control (MPC) based method for path-following, specifically for 4WS vehicles. To simplify the 4WS vehicle kinematics model, we use the assumption of pure rolling and simplify it to a single-track model. We employ a high-precision linearization transformation to convert the nonlinear kinematics models to a linear control-state system. Subsequently, we design a new objective function based on the tracking error model, and formulate the control problem as an optimization problem. Finally, we convert the optimization problem into a quadratic programming (QP) form with constraints that are suitable for real-time applications. We demonstrate the effectiveness of our proposed control method through simulation experiments. © 2023 IEEE.

关键词： Quadratic programming

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