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IEEE Conference on Vehicular Technology (VTC)

作者： Zhenguo Zhang Qianqian Yang Shibo He Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China The State Key Laboratory of Industrial Control Technology Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Zhejiang University Hangzhou China

Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system’s robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.

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Design and control of a Novel 5-DoF Tilt-Rotor Quadcopter

Design and Control of a Novel 5-DoF Tilt-Rotor Quadcopter

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Chinese Automation Congress (CAC)

作者： Zhenwei Lin Wei Liu Dabo Xu Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper presents a novel 5-DoF (degrees of freedom) tilt-rotor quadcopter. Compared to traditional quadcopters, the vehicle offers more DoF by adding two servo-driven mechanisms. The extra DoF allow it to move at a constant pitch angle, thus adapting it to more scenarios, such as slope landings. In addition, the proposed tilt-rotor quadcopter can generate greater torque around the z-axis of the body coordinate system. This gives it an advantage in some scenarios, such as carrying a manipulator to turn valves in the air. In this paper, we first design and model the proposed tilt-rotor quadcopter. Then, we design the controller and control allocation algorithm for the proposed tilt-rotor quadcopter. The control allocation algorithm has clear analytical solutions, which benefits its implementation and deployment. Finally, the structure and control strategy of the tilt-rotor quadcopter are validated by an experiment.

关键词： Torque Automation 5-DOF Atmospheric modeling Valves Manipulators Resource management Quadrotors

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VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent systems for Transport Automation

arXiv

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arXiv 2024年

作者： Chang, Cheng Zhang, Jiawei Ge, Jingwei Zhang, Zuo Wei, Junqing Li, Li Wang, Fei-Yue DiDi Autonomous Driving Company Beijing100094 China The Department of Automation BNRist Tsinghua University Beijing100084 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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Chinese control Conference (CCC)

作者： De-Xiu Wang Qing-Hao Meng Rui-Xue Han Hong-Yue Li Hui-Rang Hou Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Navigation Instruments Research Institute Tianjin China

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations.

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Task Offloading Optimization for Mobile Edge Computing System: A CNO Approach

Task Offloading Optimization for Mobile Edge Computing Syste...

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International Conference on Communication Technology (ICCT)

作者： Changqing Long Wenchao Meng Yongmin Zhang College of Control Science and Engineering Zhejiang University Hangzhou China The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province School of Computer and Engineering Central South University Changsha China

Mobile edge computing (MEC) can extend computing power at the edge of wireless networks by migrating compute-intensive tasks to MEC servers, which has the potential to enable compute-intensive applications in 5G networks. However, whether to offload tasks and how much edge computing resources to allocate to it is an NP-hard problem. In this paper, a MEC system enabled multi-user wireless network is proposed, in which the access point (AP) is equipped with a MEC server that provides computing resources for mobile users through task offloading. Then, the issue of task offloading and resource allocation is formulated as a mixed integer nonlinear program to minimize the overhead of the entire MEC system, represented by a weighted sum of delay and energy consumption. Furthermore, a collaborative neurodynamic optimization (CNO) with levy mutation operator (CNO-LMO) algorithm is proposed for searching global optimal solutions to the formulated problem. Finally, the experimental results are given to verify the superiority of the proposed CNO-LMO in reducing the overhead of the MEC system compared to some other algorithms.

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UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite

arXiv

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arXiv 2023年

作者： Guo, Sicen Li, Jiahang Feng, Yi Zhou, Dacheng Zhang, Denghuang Chen, Chen Su, Shuai Zhu, Xingyi Chen, Qijun Fan, Rui Group The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Control Science & Engineering Tongji University Shanghai201804 China The College of Electronics & Information Engineering Tongji University Shanghai201804 China The Department of Road & Airport Engineering The Key Laboratory of Road & Traffic Engineering of Ministry of Education Tongji University Shanghai200092 China

In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Sampled-Data Semi-Global Practical Tracking for Uncertain Euler-Lagrange systems

Sampled-Data Semi-Global Practical Tracking for Uncertain Eu...

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Chinese Automation Congress (CAC)

作者： Kaiji Zeng Wei Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

We investigate a sampled-data tracking control for uncertain Euler-Lagrange systems in this paper. What makes our result interesting is that our system and exosystem encompass some uncertain parameters that belong to some compact domain that can be specified arbitrarily large. To handle the uncertainties in both the Euler-Lagrange systems and the exosystem, we need to resort to not only the adaptive control technique but also the internal model method. By proposing a sampled-data control law, we solve the problem with guaranteed tracking performance, that is, the tracking errors can converge exponentially, by any fast specified rate, to any small specified neighborhood of the origin.

关键词： Adaptation models Upper bound Uncertainty Automation control systems Adaptive control

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Uncertainty and Reliability Analysis of Neural networks Based Soft-sensors With Applications in Wastewater Treatment*

Uncertainty and Reliability Analysis of Neural Networks Base...

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Chinese Automation Congress (CAC)

作者： Yiqi Liu Jingyi Yuan Daoping Huang Key Laboratory of Autonomous Systems and Networked Control Ministry of Education the School of Automation Science & Engineering South China University of Technology Guangzhou China

To overcome the transparency and interpretability issues of neural network-based soft sensing, a data-driven framework by combining neural network prediction models and sensitivity analysis methods is constructed in this paper, with application in wastewater treatment plant analysis. The results show that readily biodegradable substrate effluent (SS-e) and ${\text{N}}{{\text{H}}_4}^ + + {\text{N}}{{\text{H}}_3}$ nitrogen effluent (SNH-e) are both important input factors affecting the difficult-to-measured effluent quality, chemical oxygen demand (COD), by resorting to PAWN sensitivity analysis and four neural network models. In addition, four neural network models worked together with bilateral sensitivity analysis for the input perturbation analysis, which shows that the addition of Gaussian perturbation to SS-e and flow rate influent (Q-in) has a greater effect on the prediction which results large fluctuations of output results. Finally, the effectiveness of this framework is confirmed based on above results.

关键词： Analytical models Uncertainty Sensitivity analysis Biological system modeling Perturbation methods Neural networks Effluents

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SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor

arXiv

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arXiv 2024年

作者： Wang, Han-Guang Hou, Hui-Rang Jin, Li-Cheng Xu, Chen-Yang Zhang, Zhong-Yi Meng, Qing-Hao Zhang, Yong-Hui Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Psychiatry Tianjin Anding Hospital Mental Health Center Tianjin Medical University China

Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of diagnosis, researchers have begun to experiment with deep learning-based methods for identifying depressive disorders in recent years. Methods: In this study, a novel Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) is proposed. SAD-TIME incorporates an automated nodes’ common features extractor (CFE), a spatial sector (SpS), a modified temporal sector (TeS), and a domain adversarial learner (DAL). The CFE includes a multiscale depth-wise 1D-convolutional neural network and a time-interval embedding generator, where the unique information of each channel is preserved. The SpS fuses the functional connectivity with the distance-based connectivity containing spatial position of EEG electrodes. A multi-head-attention graph convolutional network is also applied in the SpS to fuse the features from different EEG channels. The TeS is based on long short-term memory and graph transformer networks, where the temporal information of different time-windows is fused. Moreover, the DAL is used after the SpS to obtain the domain-invariant feature. Results: Experimental results under tenfold cross-validation show that the proposed SAD-TIME method achieves 92.00% and 94.00% depression classification accuracies on two datasets, respectively, in cross-subject mode. Conclusion: SAD-TIME is a robust depression detection model, where the automatedly-generated features, the SpS and the TeS assist the classification performance with the fusion of the innate spatiotemporal information in the EEG signals. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： Computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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