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International Conference on control, Decision and information Technologies (CoDIT)

作者： Huihuang Cai Wei Chen Huanglong Fu Hua Wang Jingcheng Wang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The hydraulic system of the controllable pitch propeller (CPP) serves as the power source for adjusting the blade pitch. Due to its structural complexity and harsh operating conditions, any malfunction of this system could significantly impair the vessel's navigation. Apart from the pitch command and the pitch feedback of CPP itself, the number and types of sensors added for fault diagnosis are not fixed. Therefore, this study establishes a multimodal fault diagnosis model based on Gramian angular field(GAF). By considering the deviation values of pitch feedback and pitch command as key variables, this model transforms them into a two-dimensional image using GAF, thereby obtaining more effective feature information. Image features are extracted using ResNet, while the temporal features of additional sensors are extracted using GRU. Finally, a multi-head attention mechanism is employed for feature fusion. This method achieves a classification accuracy of 100% in the classification of four types of hydraulic system faults simulated on the experimental platform.

关键词： Fault diagnosis Propellers Navigation Hydraulic systems Transforms Sensor phenomena and characterization Feature extraction Sensor systems Marine vehicles Load modeling

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Efficient Frame Optimization Scheme for PSAM Assisted Transmission over Rician Fading Channel

Efficient Frame Optimization Scheme for PSAM Assisted Transm...

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information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Yuqian Yan Zhongyang Yu Mengmeng Xu Hengzhou Xu Hai Zhu Chunhua Zhu School of Computer Science Henan University of Engineering Zhengzhou China Key Laboratory of Grain Information Processing Control (Henan University of Technology) Ministry of Education Zhengzhou China

ISBN: (数字)9798350351699

ISBN: (纸本)9798350351705

In this paper, we discuss the commonly used pilot-symbol-assisted-modulation (PSAM) assisted transmission and its corresponding frame optimization problem over Rician fading channel. Specifically, we design a generalized PSAM (G-PSAM) format based on the standard PSAM (S-PSAM) format and derive its approximate joint data-aided & non-data-aided Cramér-Rao bound (DA&NDA CRB) when the pilot-aided channel estimation is considered. Then, with the approximate DA&NDA CRB and the classical control-variate method (CVM), an efficient optimization scheme of the G-PSAM format is proposed, which is asymptotically optimal in terms of the mean-square error (MSE) estimation performance. Simulation results reveal the superiority of the optimized G-PSAM (OG-PSAM) format over the S-PSAM format for both short-packet and long-packet transmissions. Also, a practical example of the joint DA&NDA aided carrier synchronization is provided.

关键词： Fading channels Simulation Rician channels Estimation Channel estimation Mean square error methods Synchronization

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SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

arXiv

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arXiv 2023年

作者： Song, Liang Wang, Guangming Liu, Jiuming Fu, Zhenyang Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes. © 2023, CC BY.

关键词： Rendering (computer graphics)

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Energy-Efficiency Resource Allocation for D2D Communications Under-Laying UAV Networks 13th

Energy-Efficiency Resource Allocation for D2D Communications...

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13th International Conference on Computer Engineering and Networks, CENet 2023

作者： Zhao, Pan Chen, Liuyuan Jiang, Zhiliang Han, Ming Yuan, Fucai Li, Aocheng Jia, Xiangyuan Fu, Xinqi Han, Jingqi Han, Jiaqi College of Mechanical and Electrical Engineering Henan University of Technology Zheng Zhou450001 China Henan Aerospace Hydraulic and Pneumatic Technology Co. Ltd. Zhengzhou China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou China Key Laboratory of Henan Province for Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China School of Applied Science Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9789819992386

Device-to-Device (D2D) communications under-laying Unmanned aerial vehicle (UAV) with its mobility extend the coverage and improve system capacity and flexible networking capabilities. However, the energy-efficiency are major challenge in UAV-assisted D2D networks. In this paper, the joint power and channel optimization problem has been investigated to maximize the system energy efficiency. Using the properties of objective function, the original optimization problem is transformed into two sub-problems. In the first step, the optimal power allocation problem is solved by using the Nash balance point of non-cooperative Game theory. In the second step, the channel allocation problem is constructed into a Bipartite graph, and then the Hungarian algorithm is used to find the best matching. The numerical simulation results show that the proposed scheme not only improves the energy efficiency of the system but also enhances the spectral efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Energy efficiency

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Infinite-Horizon Optimal Wireless control Over Shared State-Dependent Fading Channels for IIoT systems

arXiv

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arXiv 2024年

作者： Wang, Shuling Li, Peizhe Zhu, Shanying Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Heterogeneous systems consisting of a multiloop wireless control system (WCS) and a mobile agent system (MAS) are ubiquitous in Industrial Internet of Things systems. Within these systems, the positions of mobile agents may lead to shadow fading on the wireless channel that the WCS is controlled over and can significantly compromise its performance, requiring joint coordination between the WCS and MAS. Such coordination introduces different time steps and hybrid state spaces consisting of logical components and continuous components. This paper focuses on the infinite-horizon optimal control of MAS to ensure the performance of WCS while minimizing an average cost for the heterogeneous system subject to safety constraints. A state-dependent fading channel is modeled to capture interference among transmission links, as well as the effects of mobile agents’ movements on successful wireless transmission. In order to address the heterogeneous system dynamics, the optimal control problem is formulated as the optimal constrained set stabilization of the MAS by establishing a necessary and sufficient condition for the Lyapunov-like performance of WCS with the expected decay rates. Using the semi-tensor product of matrices, a constrained optimal state transition graph is constructed to encode the constrained system dynamics as well as objective function, which further reduces the problem into a minimum-mean cycle problem for the graph. By studying the properties of the graph, the feasibility is proven, and an effective algorithm is proposed for the construction of optimal input sequences. An illustrative example is provided to demonstrate effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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A Dual-band Monopole Antenna with EBG for Wearable Wireless Body Area Networks

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Applied Computational Electromagnetics Society Journal 2021年第1期36卷 48-54页

作者： Wang, Chao Zhang, Liang Wu, Shenbing Huang, Shijie Liu, Changqing Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei City China AnHui Province Key Laboratory of Simulation and Design Electronic Information System Hefei Normal University Hefei City China

This paper proposes a dual-band wearable monopole antenna adopting an electromagnetic band-gap (EBG) structure, which operates at 2.45 and 5.8 GHz ISM bands and is suitable for wearable applications. Both the monopole antenna and the EBG structure are fabricated on an F4B semi-flexible substrate having a dielectric constant of 2.2. The EBG structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 97.5%. This improves the antenna gain and the peak gain reaches 9.1 dBi at 5.8 GHz. The wearable performance of the antenna showed that it can sustain good performance even under realistic human body loading. Besides, the antenna has a small size, which makes it ideal for wearable applications. © ACES

关键词： Monopole antennas

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Heuristic Learning for Co-Design Scheme of Optimal Sequential Attack

arXiv

引用

arXiv 2023年

作者： Luo, Xiaoyu Pan, Haoxuan Fang, Chongrong Zhao, Chengcheng Cheng, Peng He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China

This paper considers a novel co-design problem of the optimal sequential attack, whose attack strategy changes with the time series, and in which the sequential attack selection strategy and sequential attack signal are simultaneously designed. Different from the existing attack design works that separately focus on attack subsets or attack signals, the joint design of the attack strategy poses a huge challenge due to the deep coupling relation between the sequential attack selection strategy and sequential attack signal. In this manuscript, we decompose the sequential co-design problem into two equivalent sub-problems. Specifically, we first derive an analytical closed-form expression between the optimal attack signal and the sequential attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack signal and saves computing resources. Finally, we exploit its relationship to design a heuristic two-stage learning-based joint attack algorithm (HTL-JA), which can accelerate realization of the attack target compared to the one-stage proximal-policy-optimization-based (PPO) algorithm. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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A Game Theory-Reinforcement Learning Approach to Cooperation for UAVs

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9864-9869页

作者： Tang, Changbing Pan, Linchao Chen, Jie Liu, Yang Lai, Jingang The College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China The School of Mathematical Sciences Zhejiang Normal University Jinhua321004 China The Hangzhou School of Automation Zhejiang Normal University Hangzhou311231 China The College of Electronic Engineering National University of Defense Technology Hefei230037 China The School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Education Ministry of China China Huazhong University of Science and Technology Wuhan430074 China

To execute a variety of collaborative tasks, the cooperation for unmanned aerial vehicles (UAVs) with complicated interactions under dynamic environments is a challenging and critical issue. This paper studies the cooperation issue for UAVs under dynamic environments through an approach of game theory-reinforcement learning (GT-RL) approach, which combines the advantages of both game theory and reinforcement learning. First, to cope with the complicated interactions of UAVs, the cluster of UAVs is modeled as a public goods game with asymmetrical environmental feedback. Then, reinforcement learning is adopted to optimize decision-making of UAVs under unknown and dynamic environments through comparing the dynamic behaviors of UAVs, where a novel dynamics system offers a more comprehensive understanding on cooperative behavior among UAVs. Finally, the simulation results show that the GT-RL approach can effectively promote cooperation among UAVs in completing the collaborative tasks. © 1967-2012 IEEE.

关键词： Adversarial machine learning

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Active Target Tracking Using Bearing-only Measurements With Gaussian Process Learning

arXiv

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arXiv 2024年

作者： Fu, Yingbo Yang, Ziwen Zhu, Shanying Guo, Yi Chen, Cailian The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

This paper studies the tracking problem of a target with the partially unknown motion model by an active agent with bearing-only measurements using Gaussian process learning. To address this problem, a learning-planning-control framework is proposed. First, to learn and predict the target motion under mild assumptions, a Gaussian-process-based scheme is proposed, and a probabilistic uniform prediction error bound can be rigorously proved. Second, by analyzing the data dependence of the posterior covariance, we obtain an optimal relative trajectory to achieve efficient sampling. Third, to realize efficient learning, a controller to track the planned path is proposed based on the learned target motion, which can provide guaranteed tracking performance. Theoretical analysis is conducted to prove the the given probabilistic error bounds. Numerical examples and comparison with other typical methods verify the feasibility and superior performance of our proposed framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Target tracking

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Simultaneously Infer Cell Pseudotime,Velocity Field and Gene Interaction from Multi-Branch scRNA-seq Data with scPN

arXiv

引用

arXiv 2024年

作者： Zhou, Zhen Li, Jiachen Xin, Hongyi Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Modeling cellular dynamics from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cell development and underlying gene regulatory relationships. Many current methods rely on single-cell velocity to obtain pseudotime, which can lead to inconsistencies between pseudotime and velocity. It is challenging to simultaneously infer cell pseudotime and gene interaction networks, especially in multi-branch differentiation scenarios. We present single-cell Piecewise Network (scPN), a novel high-dimensional dynamical modeling approach that iteratively extracts temporal patterns and inter-gene relationships from scRNA-seq data. To tackle multi-branch differentiation challenges, scPN models gene regulatory dynamics using piecewise gene-gene interaction networks, offering an interpretable framework for deciphering complex gene regulation patterns over time. Results on synthetic data and multiple scRNA-seq datasets demonstrate the superior performance of scPN in reconstructing cellular dynamics and identifying key transcription factors involved in development compared to existing methods. To the best of our knowledge, scPN is the first attempt at modeling that can recover pseudotime, velocity fields, and gene interactions all at once on multi-branch datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Transcription

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