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Simulated Ising Annealing Algorithm with Gaussian Augmented Hamiltonian Monte Carlo

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Simulated Ising Annealing Algorithm with Gaussian Augmented ...

Chinese control Conference (CCC)

作者： Langyu Li Hao Wang Zhixin Xie Zixuan Liu Wei Cui Yu Pan State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China School of Mechanical Engineering and Automation Harbin Institude of Technology Shenzhen China

A numerical technique called Simulated Ising Annealing (SIA) uses digital computers to obtain approximations of the ground states of Ising models. The quadratic unconstrained binary optimization (QUBO) problem in combinatorial optimization, which is challenging to solve through exhaustive search, has significant linkages to the Ising model. Therefore, SIA's main objective is to identify a better solution in finite iterations. In order to enhance the performance of the simulated annealing procedure, we adopt a Gaussian augmented Hamiltonian Monte Carlo (GAHMC) method in this study. We contrasted our algorithm with Gibbs sampling and local quantum annealing method. Finally, we discuss its application on QUBO problem.

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LiDAR-inertial 3D SLAM with plane constraint for multi-story building

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arXiv 2022年

作者： Zhang, Jiashi Zhang, Chengyang Wu, Jun Jin, Jianxiang Zhu, Qiuguo State Key Laboratory Of Industrial Control And Technology Zhejiang University Institute Of Cyber-System And Control Zhejiang University Hangzhou China

The ubiquitous planes and structural consistency are the most apparent features of indoor multi-story Buildings compared with outdoor environments. In this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with plane features for the multi-story building. The framework we proposed is mainly composed of three parts: tightly coupled LiDAR-Inertial odometry, extraction of representative planes of the structure, and factor graph optimization. By building a local map and inertial measurement unit (IMU) pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements, respectively. Minimize the joint cost function to obtain the LiDAR-Inertial odometry information. Once a new keyframe is added to the graph, all the planes of this keyframe that can represent structural features are extracted to find the constraint between different poses and stories. A keyframe-based factor graph is conducted with the constraint of planes, and LiDAR-Inertial odometry for keyframe poses refinement. The experimental results show that our algorithm has outstanding performance in accuracy compared with the state-of-the-art algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost functions

Multivariate Deep Reconstruction Neural Network for Multi-step-ahead Prediction of industrial Process Quality Variables

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IFAC-PapersOnLine 2023年第2期56卷 2852-2857页

作者： Yuwei Chen Xinmin Zhang Zhihuan Song Manabu Kano State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou 310027 P. R. China Department of Systems Science Kyoto University Kyoto 606-8501 Japan

In industrial processes, deep learning has been widely used to solve the soft sensing problem. Multi-step-ahead prediction is one of the most challenging problems in the field of soft sensors. Recently, N-Beats has been proposed as a promising deep neural architecture for multi-step prediction, but it can only be used for univariate time series prediction, not for industrial soft sensor modeling. Inspired by N-Beats, a novel deep learning model, multivariate deep reconstruction neural network (MDRNN), is proposed for multivariate time series prediction in this work. MDRNN is designed on the basis of a doubly residual structure with a deep stack of fully-connect layers. MDRNN inherits the merits of N-Beats and incorporates the “doubly residual stacking” idea into the industrial soft sensor modeling to improve the prediction accuracy. To evaluate the feasibility and effectiveness of the proposed MDRNN, it is applied to the quality prediction task and validated with two real-world industrial processes. The application results demonstrated that the proposed MDRNN can achieve higher prediction accuracy compared to the existing N-Beats and Multilayer Perceptron (MLP)-based methods.

关键词： Soft sensor multi-step-ahead prediction multivariate time series deep learning multivariate deep reconstruction neural network

GotFlow3D: Recurrent Graph Optimal Transport for Learning 3D Flow Motion in Particle Tracking

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arXiv 2022年

作者： Liang, Jiaming Xu, Chao Cai, Shengze Hangzhou310027 China The State Key Laboratory of Industrial Control Technology The National Engineering Center for Industrial Automation The Institute of Cyber-Systems & Control at ZJU ZJU Huzhou Institute China The Institute of Cyber-Systems & Control College of Control Science & Engineering Zhejiang University Hangzhou310027 China

Flow visualization technologies such as particle tracking velocimetry (PTV) are broadly used in understanding the all-pervasiveness three-dimensional (3D) turbulent flow from nature and industrial processes. Despite the advances in 3D acquisition techniques, the developed motion estimation algorithms in particle tracking remain great challenges of large particle displacements, dense particle distributions and high computational cost. By introducing a novel deep neural network based on recurrent Graph Optimal Transport, called GotFlow3D, we present an end-to-end solution to learn the 3D fluid flow motion from double-frame particle sets. The proposed network constructs two graphs in the geometric and feature space and further enriches the original particle representations with the fused intrinsic and extrinsic features learnt from a graph neural network. The extracted deep features are subsequently utilized to make optimal transport plans indicating the correspondences of particle pairs, which are then iteratively and adaptively retrieved to guide the recurrent flow learning. Experimental evaluations, including assessments on numerical experiments and validations on real-world experiments, demonstrate that the proposed GotFlow3D achieves state-of-the-art performance against both recently-developed scene flow learners and particle tracking algorithms, with impressive accuracy, robustness and generalization ability, which can provide deeper insight into the complex dynamics of broad physical and biological systems. © 2022, CC BY.

关键词： Recurrent neural networks

Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes

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arXiv 2022年

作者： Xie, Liang Yu, Hongxiang Xu, Kechun Yang, Tong Wang, Minhang Lu, Haojian Xiong, Rong Wang, Yue The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China The Application Innovate Lab Huawei Incorporated Company China

This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal sim-to-real cost. The core idea is to decouple the generalization of the sensory-motor policy to the design of a fast-adaptable perception module and a simulated generic policy module. The framework consists of a segmentation network (SN), a virtual sensor network (VSN), and a controller network (CN). Concretely, the VSN is trained to measure the pose of the unseen shape from a segmented image. After that, given the shape-agnostic pose measurement, the CN is trained to achieve generic peg-in-hole. Finally, when applying to real unseen holes, we only have to fine-tune the SN required by the simulated VSN+CN. To further minimize the transfer cost, we propose to automatically collect and annotate the data for the SN after one-minute human teaching. Simulated and real-world results are presented under the configurations of eye-to/in-hand. An electric vehicle charging system with the proposed policy inside achieves a 10/10 success rate in 2∼3s, using only hundreds of auto-labeled samples for the SN transfer. © 2022, CC BY-NC-SA.

关键词： Sensor networks

Automatic depth optimization for a quantum approximate optimization algorithm

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Physical Review A 2022年第3期105卷 032433-032433页

作者： Yu Pan Yifan Tong Yi Yang State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China Department of Mathematics and Statistics McGill University Montreal H3A 0B9 QC Canada

Quantum approximate optimization algorithm (QAOA) is a hybrid algorithm whose control parameters are classically optimized. In addition to the variational parameters, the right choice of hyperparameter is crucial for improving the performance of any optimization model. control depth, or the number of variational parameters, is considered as the most important hyperparameter for QAOA. In this paper we investigate the control depth selection with an automatic algorithm based on proximal gradient descent. The performances of the automatic algorithm are demonstrated on seven-node and ten-node max-cut problems, which show that the control depth can be significantly reduced during the iteration whereas achieving an sufficient level of optimization accuracy. With theoretical convergence guarantee, the proposed algorithm can be used as an efficient tool for choosing the appropriate control depth as a replacement of random search or empirical rules. Moreover, the reduction of control depth will induce a significant reduction in the number of quantum gates in circuit, which improves the applicability of QAOA on noisy intermediate-scale quantum devices.

关键词： Machine learning Quantum algorithms Quantum control

Learning to Fill the Seam by Vision: Sub-millimeter Peg-in-hole on Unseen Shapes in Real World

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arXiv 2022年

作者： Xie, Liang Yu, Hongxiang Zhao, Yinghao Zhang, Haodong Zhou, Zhongxiang Wang, Minhang Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China The Application Innovate Lab Huawei Incorporated Company China

In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human's behavior, we design architectures with position and orientation estimators based on the seam representation for pose alignment, which proves to be general to the unseen peg geometries. By putting the estimators into the closed-loop control with reinforcement learning, we further achieve higher or comparable success rate, efficiency, and robustness compared with the baseline methods. The policy is trained totally in simulation without any manual intervention. To achieve simto-real, a learnable segmentation module with automatic data collecting and labeling can be easily trained to decouple the perception and the policy, which helps the model trained in simulation quickly adapting to the real world with negligible effort. Results are presented in simulation and on a physical robot. Code, videos, and supplemental material are available at https://***/xieliang555/***. © 2022, CC BY-NC-ND.

关键词： Visual communication

Leveraging BEV Representation for 360-degree Visual Place Recognition

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arXiv 2023年

作者： Xu, Xuecheng Jiao, Yanmei Lu, Sha Ding, Xiaqing Xiong, Rong Wang, Yue The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China The School of Information Science and Engineering Hangzhou Normal University Hangzhou311121 China Alibaba Group Hangzhou310052 China

This paper investigates the advantages of using Bird’s Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features using standard convolutional networks and combines the features according to pre-defined 3D grid spatial points. To alleviate the mechanical and time misalignments between cameras, we further introduce deformable attention to learn the compensation. Upon the BEV feature representation, we then employ the polar transform and the Discrete Fourier transform for aggregation, which is shown to be rotation-invariant. In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets, including on-the-road and off-the-road scenarios. The experimental results verify the hypothesis that BEV can benefit VPR by its superior performance compared to baseline methods. To the best of our knowledge, this is the first trial of employing BEV representation in this task. © 2023, CC BY-SA.

关键词： Discrete Fourier transforms

Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free