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arXiv 2022年

作者： Saveriano, Matteo Abu-Dakka, Fares J. Kyrki, Ville Automatic Control Lab. Department of Industrial Engineering University of Trento Trento Italy Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Finland

In this paper, we propose an approach to learn stable dynamical systems evolving on Riemannian manifolds. The approach leverages a data-efficient procedure to learn a diffeomorphic transformation that maps simple stable dynamical systems onto complex robotic skills. By exploiting mathematical tools from differential geometry, the method ensures that the learned skills fulfill the geometric constraints imposed by the underlying manifolds, such as unit quaternion (UQ) for orientation and symmetric positive definite (SPD) matrices for impedance, while preserving the convergence to a given target. The proposed approach is firstly tested in simulation on a public benchmark, obtained by projecting Cartesian data into UQ and SPD manifolds, and compared with existing approaches. Apart from evaluating the approach on a public benchmark, several experiments were performed on a real robot performing bottle stacking in different conditions and a drilling task in cooperation with a human operator. The evaluation shows promising results in terms of learning accuracy and task adaptation capabilities. Copyright © 2022, The Authors. All rights reserved.

关键词： Robotics

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One-Shot Three-Dimensional Measurement Based on Spectral Interferometry Using a Digital Micromirror Device

One-Shot Three-Dimensional Measurement Based on Spectral Int...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Jinxu Zhang Liheng Shi Yingying Gu Donglai Zhang Lu Yin Guanhao Wu Department of Precision Instrument State Key Laboratory of Precision Measurement Technology and Instruments Tsinghua University Beijing China Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing China National Key Laboratory of Space Intelligent Control China Academy of Space Technology Beijing China

We present a one-shot 3D spectral interferometry technique using a digital micromirror device. By encoding the distribution of the spatial light field, this approach enables 3D imaging of step structures within 27 ms.

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Optical interferometry Three-dimensional displays Imaging Surface emitting lasers Measurement by laser beam Encoding Microcavities

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A New Collision Detection Algorithm for Vascular Interventional Surgery Simulation Training System 18

A New Collision Detection Algorithm for Vascular Interventio...

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18th IEEE International Conference on Mechatronics and Automation, ICMA 2021

作者： Guo, Jian Wang, Zhentao Guo, Shuxiang Tianjin University of Technology Tianjin Key Lab. for Contr. Theor. Applic. in Complicated Systems and Intelligent Robot Laboratory Binshui Xidao 391 Tianjin China Departinent of Intelligent Mechanical Systems Engineering Faculty of Engineering Kagawa University 2217-20 Hayashi-cho Takamatsu761-0396 Japan

ISBN: (纸本)9781665441001

In virtual vascular interventional surgery, collision detection between surgical instruments and human soft tissue is the basis for deformation and pressure calculation, and it is also a prerequisite for model cutting. Due to the high calculation and complexity in the virtual vascular surgery simulation process, the existing collision detection algorithms can not satisfy the requirements of real-time and accuracy during surgery. Drawing on the deficiencies and improvements of the soft tissue object collision detection algorithm, and combining the unique characteristics of the soft tissue deformation model, a new soft tissue hybrid collision detection algorithm is proposed. Through the two stages of rough detection and precise detection, a large number of redundant calculations in the original collision detection algorithm are omitted, and the response time of collision detection is greatly reduced. Experiments are compared with the other two algorithms to verify that its real-time performance and accuracy have been improved. © 2021 IEEE.

关键词： Tissue

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Hazard Analysis of Collab.rative Automation systems: A Two-layer Approach based on Supervisory control and Simulation

Hazard Analysis of Collaborative Automation Systems: A Two-l...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Tom P. Huck Yuvaraj Selvaraj Constantin Cronrath Christoph Ledermann Martin Fabian Bengt Lennartson Torsten Kröger Intelligent Process Automation and Robotics Lab Institute of Anthropomatics and Robotics (IAR-IPR) Karlsruhe Institute of Technology India Department of Electrical Engineering Division of Systems and Control Chalmers University of Technology Gothenburg Sweden

Safety critical systems are typically subjected to hazard analysis before commissioning to identify and analyse potentially hazardous system states that may arise during operation. Currently, hazard analysis is mainly based on human reasoning, past experiences, and simple tools such as checklists and spreadsheets. Increasing system complexity makes such approaches decreasingly suitable. Furthermore, testing-based hazard analysis is often not suitable due to high costs or dangers of physical faults. A remedy for this are model-based hazard analysis methods, which either rely on formal models or on simulation models, each with their own benefits and drawbacks. This paper proposes a two-layer approach that combines the benefits of exhaustive analysis using formal methods with detailed analysis using simulation. Unsafe behaviours that lead to unsafe states are first synthesised from a formal model of the system using Supervisory control Theory. The result is then input to the simulation where detailed analyses using domain-specific risk metrics are performed. Though the presented approach is generally applicable, this paper demonstrates the benefits of the approach on an industrial human-robot collab.ration system.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function lab.ls are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific lab.ls, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function lab.ls at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Generalized Predictive control Design with Colored Noise: A Simulated Case Study for Spacecraft Emulation

Generalized Predictive Control Design with Colored Noise: A ...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Antonio Silveira Marco Sagliano Rodrigo Trentini David Seelbinder Stephan Theil Lab. of Control and Systems Federal University of Para (UFPA) Belém Brazil Dept. of Navigation and Control Systems German Aerospace Center (DLR) Bremen Germany Dept. of Electrical Engineering Federal Institute of Santa Catarina (IFSC) Jaraguá do Sul Brazil

In this work the Generalized Predictive control (GPC) is revisited in order to assess its capabilities in handling colored noise disturbances using a novel design procedure. The proposed method is investigated in a simulated case study of spacecraft emulation. Such emulation is proposed by combining a network-controlled quadcopter and a set of computer-based control algorithms that impose approximated spacecraft dynamics to the aerial system. The corresponding model was built upon real registered flight data. The contribution of this paper is twofold: first, we propose a novel GPC design in the colored noise case. Second, we assess the use of quadcopters to investigate spacecraft guidance and control algorithms. Simulation results confirmed the proposed methodology as a possible low-budget spacecraft-emulation alternative for a future experimental setup. One of the main findings of the current investigation is that no significant enhancements coming from the use of GPC in minimizing the chattering of the control signal under the colored noise disturbance case could be observed. An important discussion on this matter and possible solutions are presented for future investigations.

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual lab.ling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise lab.l Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Learning Robust Data-based LQG controllers from Noisy Data

arXiv

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arXiv 2023年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie The National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States The Department of Control Science and Engineering Tongji University Shanghai201804 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

This paper addresses the joint state estimation and control problems for unknown linear time-invariant systems subject to both process and measurement noise. The aim is to redesign the linear quadratic Gaussian (LQG) controller based solely on data. The LQG controller comprises a linear quadratic regulator (LQR) and a steady-state Kalman observer;while the data-based LQR design problem has been previously studied, constructing the Kalman gain and the LQG controller from noisy data presents a novel challenge. In this work, a data-based formulation for computing the steady-state Kalman gain is proposed based on semi-definite programming (SDP) using some noise-free input-state-output data. Additionally, a data-based LQG controller is developed, which is shown to be equivalent to the model-based LQG controller. For cases where offline data are corrupted by noise, a robust data-based observer gain is constructed by tackling a relaxed SDP. The proposed controllers are proven to achieve robust global exponential stability (RGES) for state estimation and input-to-state practical stability (ISpS) under standard conditions. Finally, numerical tests are conducted to validate the proposed controllers' correctness and effectiveness. © 2023, CC BY.

关键词： Linear time-invariant system

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A Quantized Kernel Least-mean-square Algorithm Based on Echo State Network for Online Time-series Prediction 11

A Quantized Kernel Least-mean-square Algorithm Based on Echo...

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11th International Conference on Information Science and Technology, ICIST 2021

作者： Zhao, Chaochao Han, Min Wang, Jun Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian China

ISBN: (纸本)9781665412667

Kernel least mean square (KLMS) algorithm is a popular method for online time-series prediction. It has the advantages of high robustness, low computational complexity, model simplicity, and online learning ability. Unfortunately, as input data grows, its dictionary size increases and the computational complexity rises significantly. In addition, how to improve the adaptability in uncertain environments with noise is also one of the main challenges. In this paper, we propose a Quantized Kernel Least-mean-square Based on Echo State Network (ES-QKLMS) algorithm in response to the above problems. In the new algorithm, the reservoirs of the echo state network (ESN) are integrated with the KLMS algorithm for online prediction. The ESN reservoir can store the associated information of historical data, which improves the algorithm's ability to track temporal characteristics. Then, the vector quantization method is adopted to suppress the size of the KLMS dictionary. Moreover, the adaptive weight adjustment is used to improve the noise immunity in an uncertain environment. At last, The Lorenz time series and the El Nino-Southern Oscillation (ENSO) time series are discussed to demonstrate the efficacy of the ES-QKLMS algorithm. © 2021 IEEE.

关键词： Time series

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Research on Lightweight Small Object Detection Method with Fusion Attention Mechanism

Research on Lightweight Small Object Detection Method with F...

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Chinese Automation Congress (CAC)

作者： Haowen Dai Bingyou Liu Guoyang Wan Jingjing Qi Luanfei Wan School of Electrical Engineering Anhui Polytechnic University Wuhu China The Key Lab. of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University Wuhu China Anhui Dar Intelligent Control System Co. Ltd. Wuhu China Wuhu Institute of Technology Wuhu China

In the task of small object detection, due to the relatively small size of the objects, complex situations such as occlusion and overlap may arise in high-density scenes. These factors can significantly increase the issues of missed detections and false detections in object detection tasks. To address the aforementioned issues, this paper proposes a lightweight small object detection model, YOLOv5s-MGC. Firstly, we introduce an improved Mobilenetv3 network to replace the backbone network of YOLOv5s, aiming to improve the network's capability for effective feature extraction. Secondly, we incorporate the GSConv module into the feature fusion network to reduce model parameters and strengthen the network's ability in fusing feature information. Finally, we have integrated Convolutional Block Attention Module (CBAM) at the forefront of the detection heads, achieving the fusion of fine-grained features. We assessed our approach on the openly accessible dataset ‘SHWD’ and contrasted the results with the baseline YOLOv5s and various SOTA object detection models. Precisely, our method attains an accuracy of 91.1%, exceeding the baseline module by 1.6%. Furthermore, the module size is reduced to 1.97MB and it achieves a 52.4 FPS. In comparison to other prevailing algorithms, YOLOv5s-MGC enables efficient and accurate small object detection.

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