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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yanyan Shao Qi Ye Wenhan Luo Kaihao Zhang Jiming Chen College of Computer Science and Technology Zhejiang University of Technology Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China School of Cyber Science and Technology Sun Yatsen University Shenzhen China College of Engineering Computing & Cybernetics Australian National University Canberra ACT Australia

Understanding human interaction with objects is an important research topic for embodied Artificial Intelligence and identifying the objects that humans are interacting with is a primary problem for interaction understanding. Existing methods rely on frame-based detectors to locate interacting objects. However, this approach is subjected to heavy occlusions, background clutter, and distracting objects. To address the limitations, in this paper, we propose to leverage spatio-temporal information of hand-object interaction to track interactive objects under these challenging cases. Without prior knowledge of the general objects to be tracked like object tracking problems, we first utilize the spatial relation between hands and objects to adaptively discover the interacting objects from the scene. Second, the consistency and continuity of the appearance of objects between successive frames are exploited to track the objects. With this tracking formulation, our method also benefits from training on large-scale general object-tracking datasets. We further curate a video-level hand-object interaction dataset for testing and evaluation from 100DOH. The quantitative results demonstrate that our proposed method outperforms the state-of-the-art methods. Specifically, in scenes with continuous interaction with different objects, we achieve an impressive improvement of about 10% as evaluated using the Average Precision (AP) metric. Our qualitative findings also illustrate that our method can produce more continuous trajectories for interacting objects.

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A Decentralized Vehicle-to-Vehicle Energy Trading System Based on Efficient Sharding Services

A Decentralized Vehicle-to-Vehicle Energy Trading System Bas...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Kun Meng Lijun Sun Xiao Chen Haiqin Wu Shuaiyong Li College of Information Science and Technology Qingdao University of Science and Technology Shandong China School of Computing and Mathematical Sciences University of Leicester Leicester UK Software Engineering Institute (Shanghai Key Laboratory of Trustworthy Computing) East China Normal University Shanghai China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing China

Vehicle-to-vehicle (V2V) energy trading stands as a significant technology, allowing electric vehicles (EVs) to share energy. This balances energy demand and supply, reducing pressure on the power grid. However, two substantial challenges require attention: 1) Creating a standardized protocol universally adoptable by EV manufacturers and energy trading participants to establish a common secure and reliable platform for real-time energy trading between EVs. 2) Determining a fair and transparent pricing and transaction management mechanism, accounting for factors such as energy demand and supply, location, and user preferences. To tackle these hurdles, this paper proposes a decentralized V2V energy trading system (VETS) based on a novel high-performance sharding blockchain framework. VETS incorporates a standard energy trading management protocol, achieving trustless and transparent transaction processing. It employs a blockchain sharding and parallel architecture for high transaction throughput, catering to a large number of EVs. Furthermore, an efficient consensus algorithm is developed to minimize service delays and adapt to real-time transaction demands. A practical testbed operationalizes VETS, facilitating experimental analysis within a real-world cloud environment.

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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Impulsive control of Heterogeneous Multi-agent Systems with Saturation Constraint and Time Delay

Impulsive Control of Heterogeneous Multi-agent Systems with ...

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Chinese Automation Congress (CAC)

作者： Ranran Jiao Bo Li Xiangyong Chen Xiaowei Jiang Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper addresses the problem of leader-following consensus in heterogeneous multi-agent systems (HMAS) with input saturation and communication delay. Given the practical differences between leader and follower dimensions, we design virtual leaders to address this problem by allowing actual leaders and followers to communicate with virtual leaders separately, thus enabling indirect communication between actual leaders and followers. Additionally, the control protocol proposed in this paper takes into account the effects of input saturation and time delay that exist in real communication. At last, a simulation example is presented to demonstrate the effectiveness of the designed control protocol.

关键词： Energy consumption Protocols Automation Event detection Delay effects control systems Delays Multi-agent systems

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Adaptive Optimized Consensus control for a Class of Nonlinear Multi-Agent Systems with Asymmetric Input Saturation Constraints and Hybrid Faults

SSRN

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SSRN 2023年

作者： Tang, Fanghua Wang, Huanqing Zhang, Liang Xu, Ning Ahmad, Adil M. College of Control Science and Engineering Bohai University Liaoning Jinzhou121013 China College of Mathematical Science Bohai University Liaoning Jinzhou121013 China Institute of Information and Control Hangzhou Dianzi University Hangzhou310018 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

This article studies the adaptive optimized leader-follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric saturation constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, due to the complexity and scale of the actuators, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault for the multi-agent systems. To eliminate the effects of saturation and faults, a simplified smooth function is constructed to approximate the asymmetric saturation model, and the designed compensation signals are used to cope with the two main fault types such that the fault-tolerance and control performance of the multi-agent systems can be effectively improved. Subsequently, integrating all mechanisms into an adaptive optimized backstepping design framework, the long-term strategic utility functions and the virtual control signals generated in the backstepping procedure are approximated to the desired results by adopting the radial basis function neural network (RBFNN) and the actor-critic framework of reinforcement learning, and the weights in the actor-critic network are adjusted in the light of a classic gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through a simulation example. © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Estimating infinitesimal generators of stochastic systems with formal error bounds: A data-driven approach 24

Estimating infinitesimal generators of stochastic systems wi...

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24th ACM International Conference on Hybrid Systems Computation and control, HSCC 2021, held as part of the 14th Cyber Physical Systems and Internet-of-Things Week, CPS-IoT Week 2021

作者： Lavaei, Abolfazl Nejati, Ameneh Soudjani, Sadegh Zamani, Majid Institute for Dynamic Systems and Control Eth Zurich Switzerland Department of Electrical Engineering Technical University of Munich Germany School of Computing Newcastle University United Kingdom Department of Computer Science University of Colorado Boulder United States

ISBN: (纸本)9781450383394

In this work, we propose a data-driven technique for a formal estimation of infinitesimal generators of continuous-time stochastic systems with unknown dynamics. In the proposed framework, we first approximate the infinitesimal generator of the solution process via a set of data collected from solution processes of unknown systems. We then put some proper assumptions on dynamics of systems and quantify the closeness between the infinitesimal generator and its approximation while providing a priori guaranteed confidence bound. We show that both the time discretization and the number of data play significant roles in providing a reasonable closeness precision. Motivations and Related Works. Infinitesimal generator of a stochastic process (i.e., a partial differential operator that encodes a great deal of information about the process) plays a principal role in many analysis frameworks including: (i) stability analysis of continuous-time stochastic systems via Lyapunov functions [1], (ii) establishing similarity relations between two continuous-time stochastic systems via stochastic simulation functions [2], (iii) constructing finite abstractions of continuous-time stochastic control systems [3], and (iv) safety analysis of continuous-time stochastic systems via barrier certificates [4], to name a few. In the relevant literature, only [5] studies the estimation of infinitesimal generators, whose approach is based on the assumption of knowing the precise model and discretizing both time and state. To the best of our knowledge, our work is the first to propose a method for estimating the infinitesimal generator of stochastic systems with unknown dynamics while providing formal guarantees. The complete version of this work can be found in [6]. © 2021 Owner/Author.

关键词： Stochastic systems

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Research on Power Load Forecasting Based on Deep Learning

Research on Power Load Forecasting Based on Deep Learning

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Lanxin Lin Jingxin Yao Kun Wang Zhejiang Gongshang University Sussex Artificial Intelligence Institute Hangzhou China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao Hebei Chinese academy of sciences University of Chinese Academy of Sciences Shenyang institute of computing technology Shenyang China

In order to fully explore the time-series correlation of power load data and improve the prediction accuracy of power load, this paper proposes a neural network-based deep learning approach for power load prediction. Firstly, the relevant electric power data are obtained and divided into appropriate sample sizes, and the samples are normalized; then, a prediction model based on LSTM is built to explore the correlation between different features, and the corresponding model of this neural network is further trained and validated on the data test set; finally, a comparison between LSTM and other algorithms such as SVM, ANN, GAOS and GM are performed. The results show that the LSTM prediction algorithm can better track the trend of power load change, with higher prediction accuracy and efficiency.

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StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning

arXiv

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arXiv 2022年

作者： Huang, Yi-Hua He, Yue Yuan, Yu-Jie Lai, Yu-Kun Gao, Lin Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China School of Computer Science & Informatics Cardiff University United Kingdom

3D scene stylization aims at generating stylized images of the scene from arbitrary novel views following a given set of style examples, while ensuring consistency when rendered from different views. Directly applying methods for image or video stylization to 3D scenes cannot achieve such consistency. Thanks to recently proposed neural radiance fields (NeRF), we are able to represent a 3D scene in a consistent way. Consistent 3D scene stylization can be effectively achieved by stylizing the corresponding NeRF. However, there is a significant domain gap between style examples which are 2D images and NeRF which is an implicit volumetric representation. To address this problem, we propose a novel mutual learning framework for 3D scene stylization that combines a 2D image stylization network and NeRF to fuse the stylization ability of 2D stylization network with the 3D consistency of NeRF. We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF. It is followed by distilling the prior knowledge of spatial consistency from NeRF to the 2D stylization network through an introduced consistency loss. We also introduce a mimic loss to supervise the mutual learning of the NeRF style module and fine-tune the 2D stylization decoder. In order to further make our model handle ambiguities of 2D stylization results, we introduce learnable latent codes that obey the probability distributions conditioned on the style. They are attached to training samples as conditional inputs to better learn the style module in our novel stylized NeRF. Experimental results demonstrate that our method is superior to existing approaches in both visual quality and long-range consistency. Copyright © 2022, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Optimal Tracking Performance of Networked control Systems Under DoS and its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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Chinese Automation Congress (CAC)

作者： Xinyu Ren Bo Li Feng Zhao Xiangyong Chen Xianhe Zhang Xiaowei Jiang Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing Universities of Shandong Linyi University Linyi China Key Laboratory of Complex systems and Intelligent Computing School of Automation and Electrical Engineering Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system.

关键词： Automation Networked control systems Frequency-domain analysis Bandwidth Interference White noise Cyber-physical systems Poles and zeros MIMO communication Multi-agent systems

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Channel and space-based joint rate allocation algorithm

Channel and space-based joint rate allocation algorithm

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Chao Yuan Yu Sun Xin Lu Hui Guo Frederic Dufaux Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China Department of Computer Science University of Central Arkansas Faculty of Computing Engineering and Media (CEM) De Montfort University UK Université Paris-Saclay CNRS CentraleSupélec Laboratoire Des Signaux et Systèmes France School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Rate control is a critical component for image and video compression Particularly under limited network bandwidth conditions, bitrate control is essential to ensure efficient image transmission by effectively allocation channel resources. In this research, since both Channel and Spatial have relationship with rate allocation, we first propose a joint Channel-wise and Spatial-wise Quantization scheme to determine optimal quantization parameters. Subsequently, we develop a quantization step estimation network to obtain parameters to efficiently allocate rate according to target rate. Experiments demonstrate that our algorithm significantly improve compressed image quality with minimal bitrate distortion and achieve accurate rate control with nearly 3% average bitrate error.

关键词： Image quality Quantization (signal) Image coding Bit rate Signal processing algorithms Estimation Rate-distortion Video compression Resource management Speech processing

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