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IEEE Conference on Decision and control

作者： Wenwen Wu Shanying Zhu Shuai Liu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against attackers with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve cost-optimal distribution of resources while preserving privacy. Adopting constant stepsizes, the linear convergence property of diff-DMAC in mean square is established under the standard assumptions of Lipschitz smoothness and strong convexity. Moreover, it is theoretically proven that the proposed algorithm is ϵ-differentially private. And we also show the trade-off between convergence accuracy and privacy level. Finally, a numerical example is provided for verification.

关键词： Privacy Differential privacy Cost function Resource management Standards Convergence

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Unsupervised Learning of 3D Scene Flow from Monocular Camera

arXiv

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arXiv 2022年

作者： Wang, Guangming Tian, Xiaoyu Ding, Ruiqi Wang, Hesheng Department of Automation Institute of Medical Robotics 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 200240 China

Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how to train a scene flow network with unsupervised methods based on real-world data shows crucial significance. A novel unsupervised learning method for scene flow is proposed in this paper, which utilizes the images of two consecutive frames taken by monocular camera without the ground truth of scene flow for training. Our method realizes the goal that training scene flow network with real-world data, which bridges the gap between training data and test data and broadens the scope of available data for training. Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions. Depth estimation and camera pose estimation obtain the depth maps and camera pose between two consecutive frames, which provide further information for the next scene flow estimation. After that, we used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and Laplacian regularization loss to carry out unsupervised training of the scene flow network. To our knowledge, this is the first paper that realizes the unsupervised learning of 3D scene flow from monocular camera. The experiment results on KITTI show that our method for unsupervised learning of scene flow meets great performance compared to traditional methods Iterative Closest Point (ICP) and Fast Global Registration (FGR). The source code is available at: https://***/IRMVLab/3DUnMonoFlow. Copyright © 2022, The Authors. All rights reserved.

关键词： Unsupervised learning

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Exploring single atom catalysts of transition-metal doped phosphorus carbide monolayer for HER:A first-principles study

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Journal of Energy Chemistry 2021年第1期30卷 155-162,I0006页

作者： Dachang Chen Zhiwen Chen Xiaoxing Zhang Zhuole Lu Song Xiao Beibei Xiao Chandra Veer Singh School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072HubeiChina Department of Materials Science and Engineering University of TorontoToronto M5S 3E4OntarioCanada Department of Mechanical and Industrial Engineering University of TorontoToronto M5S 3G8OntarioCanada Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan 430068HubeiChina School of Energy and Power Engineering Jiangsu University of Science and TechnologyZhenjiang 212003JiangsuChina

Hydrogen has been identified as one of the most promising sustainable and clean energy. Developing hydrogen evolution reaction(HER) catalyst with high activity is essential for satisfying the future requirements. Considering novel advantages of two-dimensional materials and high catalytic activity of atomic transition metal, in this study, using density functional theory calculation, the HER on single transitionmetal(23 different TM atoms) doped phosphorus carbide monolayer(α-PC) has been investigated. The Volmer–Tafel and Volmer–Heyrovsky reaction mechanisms, and the stability of the most promising HER catalyst are also included. The results show that Ir-αPC with high physical and thermal stability has the most optimal value of Gibbs free adsorption energy for H atom. The relationship of d band center and the HER activity shows a volcano-like curve. The calculation of reaction energy barrier indicates that the Volmer-Heyrovsky step is more favorable than the Volmer-Tafel step.

关键词： Hydrogen evolution reaction Phosphorus carbide monolayer Transition-metal doping Density functional theory

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Discrete Continuous Nonlinear control system for Non-Affine Periodic Action Plant

Discrete Continuous Nonlinear Control System for Non-Affine ...

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2020 International Russian automation Conference, RusAutoCon 2020

作者： Eremin, E.L. Nikiforova, L.V. Shelenok, E.A. Amur State University Information and Control Systems Department Blagoveshchensk Russia Pacific National University Automation and System Engineering Department Khabarovsk Russia

ISBN: (纸本)9781728161303

In the present paper authors discusses the problem of construct the discrete algorithms of nonlinear-periodic control system for one class of non-affine continuous plants which working in the priory uncertainty conditions. As methods to solve the posed in paper problem hyperstability criterion, Ldissipativity conditions and continuous models method are used. With the help of numerical experiments it is depicted the operating quality of the proposed hybrid nonlinear-periodic control system. The obtained results may be useful for construction the automated control system for dynamic plants which mathematical model contains non-linearity with respect to input signal. The paper results may be useful for construct the control systems for the class of non-affine dynamic plants with mathematical models are equivalent discussed plant's model. © 2020 IEEE.

关键词： Quality control

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MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models

arXiv

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arXiv 2024年

作者： Li, Jiajia Lammers, Kyle Yin, Xunyuan Yin, Xiang He, Long Lu, Renfu Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Agricultural and Biological Engineering Pennsylvania State University United States United States Department of Agriculture Agricultural Research Service East LansingMI United States Department of Mechanical Engineering Michigan State University East LansingMI United States

Fruit harvesting poses a significant labor and financial burden for the industry, highlighting the critical need for advancements in robotic harvesting solutions. Machine vision-based fruit detection has been recognized as a crucial component for robust identification of fruits to guide robotic manipulation. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated using several metrics, which outperforms the existing state-of-the-art algorithms in both our MetaFruit dataset and other open-sourced fruit datasets, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset (https://***/datasets/jiajiali/metafruit) and detection framework (https://***/JiajiaLi04/FMFruit) are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024, CC BY-NC-SA.

关键词： Fruits

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Metafruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models

SSRN

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SSRN 2024年

Fruit harvesting poses a significant labor and financial burden for the industry, highlighting the critical need for advancements in robotic harvesting solutions. Machine vision-based fruit detection has been recognized as a crucial component for robust identification of fruits to guide robotic manipulation. Despite considerable progress in leveraging deep learning and machine learning techniques for fruit detection, a common shortfall is the inability to swiftly extend the developed models across different orchards and/or various fruit species. Additionally, the limited availability of pertinent data further compounds these challenges. In this work, we introduce MetaFruit, the largest publicly available multi-class fruit dataset, comprising 4,248 images and 248,015 manually labeled instances across diverse U.S. orchards. Furthermore, this study proposes an innovative open-set fruit detection system leveraging advanced Vision Foundation Models (VFMs) for fruit detection that can adeptly identify a wide array of fruit types under varying orchard conditions. This system not only demonstrates remarkable adaptability in learning from minimal data through few-shot learning but also shows the ability to interpret human instructions for subtle detection tasks. The performance of the developed foundation model is comprehensively evaluated using several metrics, which outperforms the existing state-of-the-art algorithms in both our MetaFruit dataset and other open-sourced fruit datasets, thereby setting a new benchmark in the field of agricultural technology and robotic harvesting. The MetaFruit dataset and detection framework are open-sourced to foster future research in vision-based fruit harvesting, marking a significant stride toward addressing the urgent needs of the agricultural sector. © 2024, The Authors. All rights reserved.

关键词： Fruits

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MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis

arXiv

引用

arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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Knowledge-Based Scene Text Recognition for Industrial Applications

Knowledge-Based Scene Text Recognition for Industrial Applic...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Guowei Deng Jingzheng Tu Cailian Chen Jianping He Xinyi Le Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (纸本)9781728119496

Scene text recognition (STR) methods combined with semantic information have made great progress to recognize texts in natural scenes, most of which are daily words. However, research on mining semantic information in industrial texts attracts less attention. Since industrial texts follow a different semantic pattern defined by industry standards, it challenges many existing methods to conduct accurate semantic reasoning. In this paper, we abstract the industry standards into two aspects of prior knowledge: the grouping property and the prior lexicon. Correspondingly, a knowledge-based language model is proposed with several group-wise correlation modules and a lexicon-based reasoning module to learn semantic rules from both data and the prior knowledge. Besides, we transfer the prior knowledge into data by generating synthetic pure text datasets according to the industry standards’ rules, which introduces more prior knowledge to the language model. Furthermore, a novel STR framework is presented by combining the knowledge-based language model and an attention-based vision model. For evaluation, two industrial text datasets called CIN and SB are collected from real-world industrial field surveillance. Experiments indicate that our method’s word-level accuracy outperforms state-of-the-art methods with 15% and 10.61% on CIN and SB datasets respectively.

关键词： Industries Correlation Text recognition Surveillance Semantics Knowledge based systems Data models

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Accelerated Simulation of Non-Markovian Quantum systems Using Quantum Trajectory

Accelerated Simulation of Non-Markovian Quantum Systems Usin...

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Chinese control Conference (CCC)

作者： Yanfang Wang Bo Yue Shibei Xue Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China

Quantum trajectory can be viewed as a numerical simulation method for wavefunctions which consume smaller memories compared to density matrices in master equations. In this paper, we adopt quantum trajectory to simulate the evolution of a non-Markovian quantum system modeled by an augmented system and further use parallel computing to improve efficiency. Compared to the Lindblad master equation solver in QuTiP, it can run more efficiently using less memory. In the example of a 8-spin-chain non-Markovian system, it only takes about 0.57% of the memory footprint and 4.12% of the runtime of the solver for the Lindblad master equation.

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Accelerated Simulation of Master Equation for Open Quantum systems

Accelerated Simulation of Master Equation for Open Quantum S...

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Chinese control Conference (CCC)

作者： Bo Yue Yanfang Wang Shibei Xue Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China

Simulation of open quantum systems is undoubtedly critical to design of quantum devices and applications of quantum technologies since dynamics of open quantum systems cannot be solved analytically in general. However, the dimension of open quantum systems grows exponentially as the number of their subsystems increases or as involving complicated environments, which reduces the computational efficiency of the simulation. In this paper, we boost numerical simulation of master equations of open quantum systems from several aspects with which the total time and the occupied memory in the simulation are significantly reduced without losing precision. Our method can be applied to the simulation of non-Markovian quantum systems described by an augmented system model as well. We conduct numerical experiments on simulation of a non-Markovian spin chain. Compared with the algorithm in a well-known toolbox, QuTiP, our method can take 26.3% time and less memory under fair conditions.

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