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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Dongqi Liu Xiangjun Zeng Yaonan Wang Changsha Unviersity of Science & Technology Changsha P.R.China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha P.R.China

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

The distribution network is developing towards the direction of Internet of Things in Electricity(IoTE). As an emerging technology of the Internet of Things(IoT), edge computing has great application potential in the future IoTE. This paper presents an architecture of edge computing-driven IoTE, whose physical layer is an electrical network containing a high proportion of power electronic devices, and its information layer is a 5G-oriented power communication network containing a large amount of power terminal and mass communication data generated by the power terminal. For the physical layer, the virtual synchronous machine(VSM) algorithm are adopted to control the large number of power electronic components so as to guarantee the dynamic stability of physical power network. For the information layer, a new layered distributed information processing architecture, the Edge Computing Architecture, is introduced to address the needs of information monitoring and protection control in the smart distribution network. The architecture of the edge computing, and the applications that may be deployed in the edge computing gateways are introduced, and the challenges may be faced by the IoTE in the future development are further discussed.

关键词： Edge computing Power system stability Computer architecture Power electronics Logic gates Internet of Things Monitoring

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FlowDriveNet: An End-to-End Network for Learning Driving Policies from Image Optical Flow and LiDAR Point Flow

FlowDriveNet: An End-to-End Network for Learning Driving Pol...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shuai Wang Jiahu Qin Menglin Li Yaonan Wang University of Science and Technology of China Hefei China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China

Learning driving policies using an end-to-end network has been proved a promising solution for autonomous driving. Due to the lack of a benchmark driver behavior dataset that contains both the visual and the LiDAR data, existing works solely focus on learning driving from visual sensors. Besides, most works are limited to predict steering angle yet neglect the more challenging vehicle speed control problem. In this paper, we propose a novel end-to-end network, FlowDriveNet, which takes advantages of sequential visual data and LiDAR data jointly to predict steering angle and vehicle speed. The main challenges of this problem are how to efficiently extract driving-related information from images and point clouds, and how to fuse them effectively. To tackle these challenges, we propose a concept of point flow and declare that image optical flow and LiDAR point flow are significant motion cues for driving policy learning. Specifically, we first create an enhanced dataset that consists of images, point clouds and corresponding human driver behaviors. Then, in FlowDriveNet, a deep but efficient visual feature extraction module and a point feature extraction module are utilized to extract spatial features from optical flow and point flow, respectively. Additionally, a novel temporal fusion and prediction module is designed to fuse temporal information from the extracted spatial feature sequences and predict vehicle driving commands. Finally, a series of ablation experiments verify the importance of optical flow and point flow and comparison experiments show that our flow-based method outperforms the existing image-based approaches on the task of driving policy learning.

关键词： Image motion analysis Computer vision visualization Laser radar Fuses Predictive models Feature extraction

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Safety-Critical control for Underwater Vehicles with Model Uncertainties and External Disturbances

Safety-Critical Control for Underwater Vehicles with Model U...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Yizong Chen Zhiqiang Miao Weiwei Zhan Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center of Robot Visual Perception and Control Technology Changsha China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Safety is a crucial issue for underwater vehicles, which may be affected by narrow terrain and multiple obstacles. In addition, the model of the underwater vehicles are uncertain and susceptible to external disturbances such as water flow. This article utilizes model predictive control (MPC) and incremental nonlinear dynamic inversion (INDI) to design a robust control scheme for underwater vehicles. The position loop controller employs MPC to generate the required speed commands for the velocity loop controller. The velocity loop is designed with an INDI control scheme incorporating a second-order low-pass filter, effectively mitigating model uncertainties and external disturbances on the vehicles. Based on exponential control barrier functions (ECBFs), the input constraint and obstacle avoidance problems of underwater vehicles are solved. The results indicate that the proposed control scheme not only exhibits robustness but also effectively ensures safe obstacle avoidance.

关键词： Uncertainty Three-dimensional displays Trajectory tracking Numerical simulation Robustness Safety Numerical models Collision avoidance Vehicle dynamics Underwater vehicles

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An Intelligent Route Recommendation Method for Smart Internet-Connected Electric Vehicles Under the Coupling of Traffic and Electricity

An Intelligent Route Recommendation Method for Smart Interne...

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Chinese control Conference (CCC)

作者： Jinhuan Xie Dongqi Liu Zhenwu Yang School of Electrical and Information Engineering Changsha University of Science and Technology hunan China National Engineering Research Center for Robot Visual Perception and Control Technology hunan China

Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario, this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model based on the road traffic congestion predicted by the big data of the Internet of Vehicles. This model integrally balances the interests of users and power grid in the coupled road-electric network, and solves the optimal path scheme under different travel scenarios based on collaborative filtering algorithm and user feedback, considering the influence of vehicle departure time and remaining power in short time scale. It is experimentally demonstrated that the proposed route hybrid recommendation model has better planning effect compared with the single travel model.

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Self-Contrastive Learning Fault Diagnosis Network for Signal Imaging: A Case Study

Self-Contrastive Learning Fault Diagnosis Network for Signal...

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Chinese Automation Congress (CAC)

作者： Wenbin He Jianxu Mao Yaonan Wang Zhe Li Feng Yan Kexin Liu College of Electrical & Information Engineering and National Engineering Research Center of Robot Visual Perception & Control Technology Hunan University Hunan China College of Control Science & Engineering and State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The deep convolution method based on MSDP signal imaging has been proven to be an effective means of monitoring the robot grinding process. This method has very high requirements on the quality of imaging and requires a lot of computing resources. This paper proposes an image self-contrastive learning network that can effectively extract fault information from low-pixel fuzzy images and achieve efficient and accurate condition monitoring. The network first calculates the correlation distance matrix between two identical modified symmetrized dot pattern (MSDP) images and uses the generated similarity labels to calculate the contrast loss to extract the embedded features of the image. Finally, the classification network is trained using the cross entropy loss to achieve the classification of fault images. The method was verified on a robotic grinding platform to significantly reduce the calculation time while maintaining the recognition accuracy, which provides reliable technical support for the real-time monitoring of the robotic grinding process.

关键词： Fault diagnosis Vibrations Accuracy Image recognition Imaging Feature extraction Real-time systems robots Monitoring Image classification

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Forecasting of the Incoming Dustcarts of a Waste Transfer Station Based on SARIMA Model

Forecasting of the Incoming Dustcarts of a Waste Transfer St...

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Chinese control and Decision Conference, CCDC

作者： Jiakang Zhou Qiu Fang Han Zhu Haojie Song Yaonan Wang National Engineering Reserch Center for Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai Chengtou Environment (Group) Co.

The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation, because it is helpful for the station management and the planning of resources. In this study, a seasonal autoregressive integrated moving average (SARIMA) model is suggested to forecast the incoming dustcarts of a waste transfer station. The dataset utilized contains both the hourly-sampled quantity and proportion of residual waste dustcarts. The outcomes of single step and multi-step forecasting are examined with different performance measures in order to confirm SARIMA’s effectiveness. The experimental results show that the SARIMA model has better prediction results compared with the LSTM model. In addition, SARIMA model has high accuracy in both single step and multi-step forecasting, but multi-step forecasting is more effective for solving real-world issues because of its less time-consuming.

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Machine-learning-based detection for quantum hacking attacks on continuous-variable quantum-key-distribution systems

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Physical Review A 2023年第6期107卷 062422-062422页

作者： Chao Ding Shi Wang Yaonan Wang Zijie Wu Jingtao Sun Yiyu Mao College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha 410082 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China

Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum hacking attacks due to imperfect devices and insufficient assumptions. In this paper, we propose a universal defense strategy called a machine-learning-based attack detection scheme (MADS). Leveraging the combined advantages of density-based spatial clustering of applications with noise (DBSCAN) and multiclass support vector machines (MCSVMs), MADS demonstrates remarkable effectiveness in detecting quantum hacking attacks. Specifically, we first establish a set of attack-related features to extract feature vectors. These vectors are then utilized as input data for DBSCAN to identify and remove any noise or outliers. Finally, the trained MCSVMs are employed to classify and predict the processed data. The predicted results can immediately determine whether or not to generate a final secret key. Simulation results show that the proposed MADS can efficiently detect most quantum hacking attacks and revise the overestimated secret key rates caused by a CVQKD system without any defense strategy to obtain a tighter security bound.

关键词： Quantum cryptography

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Unsupervised Deep Homography Estimation based on Transformer

Unsupervised Deep Homography Estimation based on Transformer

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Tianjian Jiang Qiu Fang Qing Zhu Yaonan Wang Zhen Zhou Lin Chen Jiaming Zhou Yuefan Luo Chengzhong Wu National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha Hunan China Jiangxi Communication Terminal Industry Technology Research Institute.

Homography estimation is a crucial problem in computer vision, which aims to provide an optimal transformation matrix for aligning images captured from different viewpoints. Current methods extract shallow features from image pairs and introduce learnable mask modules to improve homography estimation performance. However, they struggle to capture long-term dependencies between features and comprehend the global structures of image features. A deep unsupervised homography learning framework is proposed in this paper, consisting of a weight-sharing feature extraction network and a homography estimation network based on the Transformer model. The former extracts the local features of images, while the latter learns the correlation between them and understands the global features of images, enabling the algorithm to better estimate the homography of unaligned images. Experimental results demonstrate that the proposed method outperforms the advanced methods for estimating homography matrices in the CA-Unsupervised dataset.

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Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Grasping in Dexterous robotics

arXiv

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arXiv 2024年

作者： Yang, Fan Chen, Wenrui Yang, Kailun Lin, Haoran Luo, DongSheng Tang, Conghui Li, Zhiyong Wang, Yaonan The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features of objects serve as a bridge in the functional interaction between agents and objects. However, leveraging these affordance cues to help robots achieve functional tool grasping remains unresolved. To address this, we propose a granularity-aware affordance feature extraction method for locating functional affordance areas and predicting dexterous coarse gestures. We study the intrinsic mechanisms of human tool use. On one hand, we use fine-grained affordance features of object-functional finger contact areas to locate functional affordance regions. On the other hand, we use highly activated coarse-grained affordance features in hand-object interaction regions to predict grasp gestures. Additionally, we introduce a model-based post-processing module that includes functional finger coordinate localization, finger-to-end coordinate transformation, and force feedback-based coarse-to-fine grasping. This forms a complete dexterous robotic functional grasping framework GAAF-Dex, which learns Granularity-Aware Affordances from human-object interaction for tool-based Functional grasping in Dexterous robotics. Unlike fully-supervised methods that require extensive data annotation, we employ a weakly supervised approach to extract relevant cues from exocentric (Exo) images of hand-object interactions to supervise feature extraction in egocentric (Ego) images. Correspondingly, we have constructed a small-scale dataset, FAH, which includes near 6K images of functional hand-object interaction Exo images and Ego images of 18 commonly used tools performing 6 tasks. Extensive experiments on the dataset demonstrate that our method outperforms state-of-the-art methods, and real-world localization and grasping experiments validate the practical applicability of our approach. The source code and the establishe

关键词： Extraction

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LF-PGVIO: A visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China School of Computing and Engineering University of Huddersfield Huddersfield U.K State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

In this paper, we propose LF-PGVIO, a visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. IEEE

关键词： Feature extraction

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