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

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

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A New Mutimodel approach for modelling of fractional order interval transfer functions 20

A New Mutimodel approach for modelling of fractional order i...

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20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Talmoudi, Samia Ksouri, Moufida Lahmari Applied College Department of Computer Science University of Tabuk Tabuk Saudi Arabia University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis1002 Tunisia

ISBN: (纸本)9798350332568

This paper is focused on uncertain fractional order systems. In this context, a new modelling approach of uncertain fractional order systems represented by an explicit fractional order interval transfer function is proposed. This approach is based, essentially, on multimodel approach. In fact, firstly, two new determination methods of models' library, inspired from the Kharitonov approach, are exposed. Then, we propose to compute the validity degree of each model of the obtained library, by optimizing a constrained least squares problem. The global model is, finally deduced by fusion of outputs of the different library' models. To prove its efficiency and its precision, the proposed approach is then compared to the approximation approach of Oustaloup through two simulation examples. © 2023 IEEE.

关键词： Transfer functions

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ACIGS: An automated large-scale crops image generation system based on large visual language multi-modal models

ACIGS: An automated large-scale crops image generation syste...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Bolong Liu Hao Zhang Jie Liu Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science and Technology Harbin Institute of Technology (ShenZhen) ShenZhen China

Smart agriculture requires an extensive convergence of information technology and agriculture. Attaining intelligence mandates an enormous amount of data to train models. However, it is challenging to acquire a large number of crop image data, limiting the application and growth of computer vision technology in agriculture. To address this problem, we designed a crop image generation system that combines a large language model with visual language multi-modal large models to augment the scale, variety, and resolution of crop image data. First, the system inputs existing real crop images into the visual language multimodal model to extract features and represent crop images in text form. Then, the system passes the crop text representation to the language model for cleaning and processing, which generates prompts to create crop images. The prompts are input into the visual language multi-modal model to generate crop images based on text representation of crops. The resulting crop images undergo image quality evaluation in the visual language multimodal model, and high-quality crop images are saved to the crop image dataset based on the quality evaluation. These steps lead to the formation of the final generated crop image dataset. The experimental results indicate that the crop images generated using the proposed system are similar to but different from the example images. This characteristic enables the expansion of crop data while circumventing redundancy and allowing for resolution control, which is crucial for dense segmentation tasks. Using this method, the existing data can be enlarged up to 7.5 times.

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DPPL Hallway Tracker: Hospital Contact Tracing During the COVID-19 Pandemic 9

DPPL Hallway Tracker: Hospital Contact Tracing During the CO...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Conformal Prediction for Distribution-free Optimal control of Linear Stochastic Systems

arXiv

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arXiv 2024年

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Thomas Lord Department of Computer Science The Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los AngelesCA90089 United States The School of Electronics Electrical Engineering and Computer Science Queen's University Northern Ireland Belfast United Kingdom

We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the S-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size. © 2024, CC BY.

关键词： Optimal control systems

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Pedestrian Positioning Algorithm Based on Zero-Velocity Correction 3

Pedestrian Positioning Algorithm Based on Zero-Velocity Corr...

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3rd International Conference on Automation, Robotics and computer engineering, ICARCE 2024

作者： Huang, Shiying Li, Haolin Zhang, Xueyan Liu, Hongwei Fu, Tianhua Beihang University Department of Instrumentation Science and Opto-Electronics Engineering Beijing China Beihang University Department of Computer Science Beijing China Harbin Medical University Harbin China Beijing Institute of Precision Machinery and Control Beijing China Beijing Experimental Factory Co. Ltd Beijing China

ISBN: (纸本)9798331529505

Pedestrian positioning technology is a key technology that has emerged in recent years in the field of navigation. It is of great significance in indoor, underground and other navigation fields. In this paper, aiming at the problem of error accumulation in pedestrian positioning systems based on MEMS devices, a pedestrian positioning method based on the inertial navigation scheme is studied, and the zero-velocity correction method is used to suppress the accumulation of velocity errors, thereby improving the positioning accuracy. The effectiveness of the method in this paper is verified by experiments. © 2024 IEEE.

关键词： MEMS devices Pedestrian positioning Zero velocity correction

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Complexity results and active-set identification of a derivative-free method for bound-constrained problems

arXiv

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arXiv 2024年

作者： Brilli, Andrea Cristofari, Andrea Liuzzi, Giampaolo Lucidi, Stefano Department of Computer Control and Management Engineering Sapienza University of Rome Italy Via Ariosto 25 Rome00185 Italy Department of Civil Engineering and Computer Science Engineering University of Rome "Tor Vergata" Via del Politecnico 1 Rome00133 Italy

In this paper, we analyze a derivative-free line search method designed for bound-constrained problems. Our analysis demonstrates that this method exhibits a worst-case complexity comparable to other derivative-free methods for unconstrained and linearly constrained problems. In particular, when minimizing a function with n variables, we prove that at most O (nǫ−2) iterations are needed to drive a criticality measure below a predefined threshold ǫ, requiring at most O (n2ǫ−2) function evaluations. We also show that the total number of iterations where the criticality measure is not below ǫ is upper bounded by O (n2ǫ−2). Moreover, we investigate the method capability to identify active constraints at the final solutions. We show that, after a finite number of iterations, all the active constraints satisfying the strict complementarity condition are correctly identified. Copyright © 2024, The Authors. All rights reserved.

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MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

arXiv

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arXiv 2024年

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada The School of Control Science and Engineering Shandong University Shandong China

—Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments. (Video1) Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empo

关键词： Deep neural networks

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Automatic Identification of Functionally Untestable Cell-Aware Faults in Microprocessors

Automatic Identification of Functionally Untestable Cell-Awa...

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Asian Test Symposium (ATS)

作者： Nikolaos I. Deligiannis Tobias Faller Iacopo Guglielminetti Riccardo Cantoro Bernd Becker Matteo Sonza Reorda Department of Control and Computer Engineering (DAUIN) Politecnico di Torino Turin Italy Department of Computer Science University of Freiburg Freiburg Germany STMicroelectronics Turin Italy

In-field test of microprocessors is a major topic for the industry, especially in the safety-critical domain, where the respective standards mandate high test coverage thresholds. The dominant fault models used are the transition delay and the stuck-at fault model. However, the adoption of very advanced semiconductor technologies to manufacture devices used in safety-critical applications pushes toward considering new fault models that are better suited to catch subtle and age-related defects. Among the other phenomena, latent cell-internal defects emerged as relevant causes for several failures. Hence, the necessity for the Cell-Aware Test (CAT) was born, and the inclusion of the CAT fault model in the latest safety standards. Although CAT amends the issue of the numerous test escapes, it may suffer as well from the presence of functionally untestable faults that may pollute the overall test efficiency with their presence. In this paper, we propose a solution, based on formal methods, for the automatic identification of functionally untestable faults under the Cell-Aware fault model for the case where the DUT is a fully pipelined processor. As a case study, we used the RISC-V processor RI5CY for which we applied the minimum constraints required to ensure a functional behavior to demonstrate the effectiveness and impact of the approach. With the considered constraints, a significant percentage of functionally untestable faults was located in the several modules within the processor. Furthermore, the method allows to flexibly take into account any constraint stemming from the system configuration and the application. The obtained results have been validated by resorting to commercial EDA tools.

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