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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Ye, Bao-Lin Zhang, Jiajie Li, Lingxi Wu, Weimin Jiaxing Key Laboratory of Smart Transportations the School of Information Science and Engineering Jiaxing University China Elmore Family School of Electrical and Computer Engineering Purdue University Indianapolis USA State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Trajectory planning method is a research hotspot in autonomous driving. Existing reinforcement learning-based trajectory planning methods suffer from unstable performance due to the strong randomness of network weight parameter updates during the training process. Therefore, this paper proposes a novel trajectory planning method based on deep reinforcement learning trust region policy optimization (TRPO). Firstly, in order to enhance the robustness of the trajectory planning method based on deep reinforcement learning TRPO, a TRPO-LSTM based decision model was proposed. More specifically, a long short term memory (LSTM) based state feature extraction network was designed and embeded into a TRPO-based decision model to enhance the ability of TRPO to extract information from the environmental state space. Secondly, in order to make the planned trajectory adaptive to the dynamic changes of traffic environment, we presented a novel TRPO-LSTM trajectory fitting algorithm. To the best of our knowledge, this is the first work aiming at applying the TRPO-LSTM based decision model in the trajectory fitting process to search the optimal longitudinal trajectory speed. Finally, the proposed trajectory planning method was implemented and simulated on the CARLA simulator. The experimental results show that, compared with existing trajectory planning methods based on deep reinforcement learning algorithms, our proposed method achieves a cumulative reward improvement of over 28.9% in the scenario of four lane highway, and has better robustness. Meanwhile, the proposed method can achieve a lower collision rate of 0.93% while improving the average speed and comfort of vehicle driving. IEEE

关键词： Heuristic algorithms

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

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A DCD-chip designed for digital and ultra-precise quantification of copy number variation 26

A DCD-chip designed for digital and ultra-precise quantifica...

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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Zou, Zheyu Xia, Liping Yin, Juxin Mu, Ying Research Centre for Analytical Instrumentation Institute of Cyber-Systems and Control State Key Laboratory of Industrial Control Technology Zhejiang University China College of Life Sciences Zhejiang University China School of information and Electrical Engineering Zhejiang University City College China

ISBN: (纸本)9781733419048

Copy number variation (CNV) has been associated with many kinds of disease. It is crucial to precisely quantified copy number variation of samples among patients which may could guide treatment. Digital PCR (dPCR) enables high-resolution CNV analysis through ultraprecise, absolute quantification of specific nucleic acid sequence. We explored a platform named digital CNV detection chip (DCD-chip), which can simultaneously and absolutely quantified GPR146 and RPPH1 genes. We even verified that DCD-chip was more accurate than qPCR when the samples were diluted to a certain extent, which indicated the powerful quantification capacity of our DCD-chip. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Polymerase chain reaction

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PiAGI: The Concept, Mechanism and Framework 4

PiAGI: The Concept, Mechanism and Framework

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Li, Qiang Ju, Zhuoya Fan, Lili Liu, Xin Liu, Wenhua Wang, Feiyue Scientific and Technological Innovation Center Beijing China Beijing Institute of Technology The School of Automation Beijing China China University of Petroleum School of Big Data & Cyber Security Qingdao China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As social production and living spaces gradually expand from the physical and information domains to the cognitive and social domains, this article addresses the new changes brought about by these expansions. Starting from the new impact of era of intelligent connectivity on social cognitive management, it analyzes the urgent need to enhance social cognitive management capabilities centered on cognitive decision-making, identifying the core key as Adversarial Generated Intelligence (AGI). It delves into the conceptual characteristics, operating mechanisms, and winning strategies of AGI, identifies the new challenges it faces, and proposes a Parallel Intelligence empowering AGI (PiAGI) system architecture through the empowerment of parallel intelligence. This provides theoretical support for improving social cognitive management capabilities centered on cognitive decision-making. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal Velocity control of Spherical Robots Based on Offset-free Linear Model Predictive control

Optimal Velocity Control of Spherical Robots Based on Offset...

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IEEE International Conference on Robotics and Biomimetics

作者： Tao Hu Xiaoqing Guan Boyu Lin Yixu Wang Yifan Liu You Wang Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China

ISBN: (纸本)9781665481106

Since the sensors of spherical robots rotate with the longitudinal axis, an efficient velocity controller must also account for robot's attitude. Therefore, this paper proposed an optimal velocity controller for spherical robots based on the offset-free linear model predictive control (LMPC), which controls the velocity while considering robot's attitude, motor's current and other variables. The prediction model of LMPC was generated by linearizing and discretizing the dynamic model and introducing the disturbance model. Then the kalman filter (KF) was used to estimate the states and disturbance. After that, combined with LMPC, the offset-free tracking of different velocities under different terrains was achieved by fully considering velocity, attitude and current in the target function. Finally, a series of physical experiments show that the proposed controller ensures fast convergence, strong stability and no bias, while the velocity changes more smoothly, the attitude changes less and the current output is more stable.

关键词： Target tracking Attitude control Biological system modeling Velocity control Predictive models Robot sensing systems Stability analysis

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Adaptive Sliding Mode control for Trajectory Tracking in Four-Wheeled Mobile Robots Under Uncertainties and External Disturbances

Adaptive Sliding Mode Control for Trajectory Tracking in Fou...

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Chinese control and Decision Conference, CCDC

作者： Naeem Ajilforoushan Saba Ghorbani Barzegar Yongliang Lin Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In this paper, we propose an adaptive sliding mode control (SMC) design technique to track the desired trajectory of the skid-steering mobile robots in the presence of model uncertainties and perturbations. To enhance the stability of the robot and ensure accurate trajectory tracking, adaptive rules have been employed to estimate the upper bound of perturbations. This estimation, in turn, serves to stabilize the robot and guarantee the desired trajectory. Moreover, we analyze and calculate the convergence time of the sliding surface, ensuring its convergence within finite time. Additionally, efforts have been made to minimize the chattering phenomenon by smoothing the discontinuity of the sliding surface. Simulation results validate the efficacy of the proposed controller, demonstrating its ability to deliver acceptable performance in diverse scenarios.

关键词： Uncertainty Upper bound Trajectory tracking Perturbation methods Estimation Stability analysis Trajectory

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Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning

arXiv

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arXiv 2022年

作者： Zhou, Zhongxiang Yang, Yifei Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

Detecting both known and unknown objects is a fundamental skill for robot manipulation in unstructured environments. Open-set object detection (OSOD) is a promising direction to handle the problem consisting of two subtasks: objects and background separation, and open-set object classification. In this paper, we present Openset RCNN to address the challenging OSOD. To disambiguate unknown objects and background in the first subtask, we propose to use classification-free region proposal network (CF-RPN) which estimates the objectness score of each region purely using cues from object’s location and shape preventing overfitting to the training categories. To identify unknown objects in the second subtask, we propose to represent them using the complementary region of known categories in a latent space which is accomplished by a prototype learning network (PLN). PLN performs instance-level contrastive learning to encode proposals to a latent space and builds a compact region centering with a prototype for each known category. Further, we note that the detection performance of unknown objects can not be unbiasedly evaluated on the situation that commonly used object detection datasets are not fully annotated. Thus, a new benchmark is introduced by reorganizing GraspNet-1billion, a robotic grasp pose detection dataset with complete annotation. Extensive experiments demonstrate the merits of our method. We finally show that our Openset RCNN can endow the robot with an open-set perception ability to support robotic rearrangement tasks in cluttered environments. More details can be found in https://***/view/openset-rcnn/ Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Multi-Objective Optimization of Operation of Power-Traffic systems Considering Dynamic Wireless Charging

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IFAC-PapersOnLine 2023年第2期56卷 6654-6659页

作者： Ze Zhou Zhitao Liu Hongye Su Liyan Zhang Wenhai Wang State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China School of Automation Wuhan University of Technology Wuhan 430070 China Zhejiang University NGICS Platform Hangzhou 310027 China

To reduce the consumption of fossil fuels, improve the environment, and optimize the energy structure, electric vehicles (EVs) have been developed rapidly. At the same time, to alleviate the range anxiety of EV users, the corresponding dynamic wireless charging (DWC) technology has also attracted extensive attention. Driven by EVs and DWC, the interdependency between the power system and transportation system becomes tighter. To improve the operation of the power-traffic system, we propose a multi-objective optimization problem incorporating transportation system congestion, power system generation cost, and total EV user charging cost. Assuming that the energy provided by the DWC can meet the consumption in EV travel, we construct a power demand model for grid buses. Furthermore, we model the microscopic characteristics of vehicle behaviors and propose an adaptive route recommendation algorithm. To solve the multi-objective problem, we adopt a non-dominated sorting genetic algorithm II. Finally, the case studies demonstrate the feasibility of the proposed multi-objective optimization problem and the effectiveness and superiority of the proposed algorithm.

关键词： Dynamic wireless charging electric vehicles multi-objective optimization power-traffic systems route recommendation algorithm

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Efficient and High-quality Neural Implicit RGB-D Surface Reconstruction with Volume Feature Rendering

Efficient and High-quality Neural Implicit RGB-D Surface Rec...

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Robotics, control and Automation (ICRCA), International Conference on

作者： Jintao Wan Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

Acquiring high-quality 3D surfaces of indoor scenes is essential for applications in VR/ AR and robotics. Recent advancements in neural implicit surface learning and differ-entiable volume rendering methods have significantly improved the accuracy and completeness of geometry. In our work, we propose a volumn feature rendering method for fast and detailed surface reconstruction from muti-view RGB-D frames. We adopt a hybrid scene representation utilizing muti-resolution hash encoding and two shallow MLPs for fast convergence. We suggest improvements by replacing standard volume rendering with vol-ume feature rendering to optimize speed. Additionally, we employ an adaptive sampling strategy guided by dynamic occupancy grids to accelerate training. We introduce a curvature term to promote surface smoothness and hole filling in untexture areas. Experiments on standard benchmarks show that our method can process room-scale scenes in approximately 10 minutes, achieving a 2x speedup compared to Go-Surf [14] in a memory-efficient way, while maintaining comparable or superior reconstruction quality.

关键词： Training Geometry Surface reconstruction Three-dimensional displays Accuracy Rendering (computer graphics) Surface treatment

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Research on optical properties of human hepatoma cells using terahertz time-domain spectroscopy

Research on optical properties of human hepatoma cells using...

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2023 International Symposium on Ultrafast Phenomena and Terahertz Waves, ISUPTW 2023

作者： Guan, Hanxiao Qiu, Weihang Ma, Yehao Tian, Liangfei Cao, Yuqi Zhang, Guangxin College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou China Ningbo University of Technology Robotics Institute Ningbo China

ISBN: (纸本)9798350317473

Terahertz technology is a non-invasive and label-free inspection method, promising for applications in various fields, especially in biomedical diagnosis. Terahertz radiation is capable for cell physiological and pathological status recognition due to its sensitivity to cell composition and structural information of biomacromolecular. In this paper, the transmission properties of glass, polymethyl methacrylate and polypropylene in THz regime were compared and polypropylene was selected as the petri dish material for cell adhesion due to its high transmission of terahertz wave. The time-domain and frequency-domain spectra, refractive index and absorption coefficient of hepatoma cell line Huh7 and normal cell line MIHA were analyzed. The experimental results showed significant distinction between the terahertz characteristic spectra of hepatoma cells and normal cells. This work can be helpful for applying terahertz technology in cancer detection at the cellular level. © 2023 IEEE.

关键词： Terahertz waves

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