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Robust quantum teleportation via a non-Markovian channel

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arXiv 2023年

作者： Wang, Yanfang Xue, Shibei Song, Hongbin Jiang, Min 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 General Education Division Shenzhen Key Lab of Semiconductor Lasers The Chinese University of Hong Kong Guang Dong Shenzhen518116 China School of Electronics and Information Engineering Soochow University Suzhou215006 China

In this paper, we propose a non-Markovian quantum channel approach to mitigating the degradation of the average fidelity in continuous-variable quantum teleportation. The non-Markovian quantum channel is modeled by an augmented system, where ancillary systems are introduced to represent the internal modes of non-Markovian environments. With a proper non-Markovianity, enhanced effects of the channels on continuous-variable quantum teleportation are observed. Also, the logarithmic negativity of entangled states can be effectively maintained so that the decay of the average fidelity of quantum teleportation is mitigated. In addition, the analysis on different teleported quantum states shows that coherent states exhibit stronger robustness than those of squeezed states and cat states in quantum teleportation with a non-Markovian channel. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum entanglement

Event-triggered consensus of matrix-weighted networks subject to actuator saturation

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arXiv 2021年

作者： Pan, Lulu Shao, Haibin Li, Yuanlong Li, Dewei Xi, Yugeng Department of Automation and the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China

The ubiquitous interdependencies among higher-dimensional states of neighboring agents can be characterized by matrix-weighted networks. This paper examines event-triggered global consensus of matrix-weighted networks subject to actuator saturation. Specifically, a distributed dynamic event-triggered coordination strategy, whose design involves sampled state of agents, saturation constraint and auxiliary systems, is proposed for this category of generalized network to guarantee its global consensus. Under the proposed event-triggered coordination strategy, sufficient conditions are derived to guarantee the leaderless and leader-follower global consensus of the multi-agent systems on matrix-weighted networks, respectively. The Zeno phenomenon can be excluded for both cases under the proposed coordination strategy. It turns out that the spectral properties of matrix-valued weights are crucial in event-triggered mechanism design for matrix-weighted networks with actuator saturation constraint. Finally, simulations are provided to demonstrate the effectiveness of proposed event-triggered coordination strategy. This work provides a more general design framework compared with existing results that are only applicable to scalar-weighted networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Actuators

Robust optimization-based fusion of GNSS and Visual-Inertial-Wheel Odometry

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Robust optimization-based fusion of GNSS and Visual-Inertial...

IEEE International Conference on Robotics and Biomimetics

作者： Jake Jie Hou Xinrui Wu Jiwei Shan Dun Li Hesheng Wang Department of Automation Shanghai Jiao Tong University China Shanghai Karlang Industrial Nantong Intelligent Technology Co. Ltd 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 Shanghai China

ISBN: (纸本)9781665481106

SLAM is significant to autonomous mobile robots. In order to realize drift-free state estimation and provide accurate localization results for the subsequent navigation, we develop an optimization-based framework consisting of a high-frequency Inertial-Wheel Odometry(IWO), Visual-Inertial-Wheel Odometry(VIWO) and a global fusion module. To fuse the local state with the position provided by GNSS, we perform ternary edge in factor graph optimization to estimate the transformation between the local frame and ENU coordinate. Thanks to the tightly-coupled VIWO, the localization property of the odometry presents more robustness, and improve the accuracy of global fusion. A noise repropagation strategy is introduced to ensure the consistency of the IMU and wheel odometer. We evaluate the proposed system on public datasets and test it in real-world scenario. The experimental results show that our system achieves accurate pose estimation in outdoor environments.

关键词： Location awareness Global navigation satellite system Simultaneous localization and mapping Robot kinematics Wheels Robustness Sensors

Improving Time Synchronization with Period Adaptation in Single Twisted Pair Ethernet

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Improving Time Synchronization with Period Adaptation in Sin...

IEEE International Conference on Networking, Sensing and control

作者： Xuchao Gao Zhezhuang Xu Guanwen Cui Jian Chen Qimin Xu College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781665440493

In the industrial internet of things (IIoT), single twisted pair Ethernet becomes popular since it can reuse single twisted pair cables in the field bus to connect field devices to the IIoT. However, in the single twisted pair Ethernet, the delay jitter greatly impacts the performance of time synchronization that can not satisfy the requirements of time-sensitive and high speed tasks in the industrial field. To solve this problem, in this paper, an improved time synchronization algorithm is proposed based on precision time protocol (PTP). By detecting the delay jitter, this algorithm could adaptively adjust the synchronization period, and thus reduce the time offset. The algorithm is deployed in the testbed to study its performance by comparing with original PTP. Experimental results prove that the proposed algorithm can increase the accuracy of the time synchronization in the single twisted pair Ethernet.

关键词： Performance evaluation Protocols Heuristic algorithms Ethernet Delay estimation Jitter Sensors

Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving

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arXiv 2021年

作者： Sheng, Zihao Xu, Yunwen Xue, Shibei Li, Dewei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles the spatial interactions using a graph convolutional network (GCN), and captures the temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our proposed scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM). Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Autonomous vehicles

Bidirectional Pricing and Demand Response for Nanogrids with HVAC systems

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arXiv 2022年

作者： Cao, Jiaxin Yang, Bo Zhu, Shanying Ma, Kai Guan, Xinping 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 School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Owing to the fluctuant renewable generation and power demand, the energy surplus or deficit in each nanogrid is embodied differently across time. To stimulate local renewable energy consumption and minimize the long-term energy cost, some issues still remain to be explored: when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors. For this purpose, the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity (PME) are studied with exploiting the large potential thermal elastic ability of heating, ventilation and air-conditioning (HVAC) units. Different from nanogrids, in terms of minimizing time-average costs, PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally. In particular, such bilevel energy management problem is formulated as a stochastic form in a long-term horizon. Since there are uncertain system parameters, time-coupled queue constraints and the interplay of bilevel decision-making, it is challenging to solve the formulated problems. To this end, we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters. The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework. Then, theoretical analysis of the existence and uniqueness of Stackelberg equilibrium (SE) is developed based on the proposed game property. Following that, we devise an optimization algorithm to reach the SE with less information exchange. Numerical experiments validate the effectiveness of the proposed approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Costs

Few-Shot Classification with Task-Adaptive Semantic Feature Learning

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SSRN

SSRN 2022年

作者： Pan, Mei-Hong Xin, Hong-Yi Xia, Chun-Qiu 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 University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiaotong University Shanghai200240 China

Few-shot classification aims to learn a classifier that categorizes objects of unseen classes with limited samples. One general approach is to mine as much information as possible from limited samples. This can be achieved by incorporating data from multiple modals. However, existing multi-modality methods only use additional modality in support samples while adhering to a single modal in query samples. Such approach could lead to information imbalance between support and query samples, which confounds model generalization from support to query samples. Towards this problem, we propose a task-adaptive semantic feature learning mechanism to incorporate semantic features for both support and query samples. The semantic feature learner is trained episodic-wisely by regressing from the feature vectors of the support samples. It is utilized to predict semantic features for the query samples. Such method maintains a consistent training scheme between support and query samples and enables direct model transfer from support to query data, which significantly improves model generalization. We conduct extensive experiments on four benchmarks in both inductive and tranductive settings. Results show that the proposed TasNet outperforms state-of-the-art methods with an improvement of 1% to 5% in classification accuracy, demonstrating the superiority of our method. The exhaustive ablation studies further validate the effectiveness of our framework. © 2022, The Authors. All rights reserved.

关键词： Machine learning

Atf-Bss: Adaptive Time-Frequency Blind Source Separation for High Reverberation and Echo Environments

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SSRN

SSRN 2023年

作者： Xie, Yuan Zou, Tao Chen, Ci Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation with high reverberation and echoes is an open and extremely challenging issue. Because the room reverberation and echoes have an important influence on acoustics quality, bringing great difficulty to the independent source separation. In the paper, an adaptive time-frequency blind source separation (ATF-BSS) algorithm is designed to deal with the high reverberation and echoes, which can adaptively attenuate the audible echoes and enhance the harmonic structure of the real-world audio signals, achieving excellent separation performance. Firstly, an equalizer network is designed for the room impulse response to weakening audible echoes under the high reverberation and echo environments without changing the perceived timbre. Furthermore, an adaptive time-frequency Wiener-like masking is presented to enhance the dominant signal and attenuate the other components having less energy than the dominant signal. To the best of our knowledge, this is the first attempt to incorporate the advantages of an equalizer network for removing or weakening audible echoes and adaptive time-frequency masking to solve blind separation with high reverberation and echoes. Experimental results on the public audio datasets demonstrate that the proposed ATF-BSS algorithm improves signal intelligibility and achieves much better separation performance than the state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Reverberation

Lattice piecewise affine approximation of explicit linear model predictive control

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Lattice piecewise affine approximation of explicit linear mo...

IEEE Conference on Decision and control

作者： Jun Xu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai

ISBN: (纸本)9781665436601

In this paper, the lattice piecewise affine (PWA) approximation of explicit linear model predictive control (MPC) is proposed. The training data consists of the state samples and corresponding affine control laws, based on which the lattice PWA approximation is constructed. The proposed approximation is identical to the explicit MPC control law in unique-order (UO) regions containing the sample points as interior points, thus resemble the explicit MPC control law in a large number of regions. Through simplifying the terms and literals in the lattice PWA approximation, both the storage requirement and online computation complexity are largely decreased. The performance of the proposed approximation strategy is tested through a simulation example, and the result shows that with a moderate number of sample points, the lattice PWA approximation is very close to the explicit MPC control law.

关键词： Conferences Computational modeling Lattices Training data Computational complexity Predictive control