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arXiv 2021年

作者： Ma, Jun Cheng, Zilong Zhu, Haiyue Li, Xiaocong Tomizuka, Masayoshi Lee, Tong Heng Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Singapore Institute of Manufacturing Technology Singapore138634 Singapore John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Mechanical Engineering University of California BerkeleyCA94720 United States

Magnetic levitation positioning technology has attracted considerable research efforts and dedicated attention due to its extremely attractive features. The technology offers high-precision, contactless, dust/lubricant-free, multi-axis, and large-stroke positioning. In this work, we focus on the accurate and smooth tracking problem of a multi-axis magnetically levitated (maglev) planar positioning system for a specific S-curve reference trajectory. The floating characteristics and the multi-axis coupling make accurate identification of the system dynamics difficult, which lead to a challenge to design a high performance control system. Here, the tracking task is achieved by a 2-Degree of Freedom (DoF) controller consisting of a feedforward controller and a robust stabilizing feedback controller with a prescribed sparsity pattern. The approach proposed in this paper utilizes the basis of an H∞ controller formulation and a suitably established convex inner approximation. Particularly, a subset of robust stabilizable controllers with prescribed structural constraints is characterized in the parameter space, and so thus the re-formulated convex optimization problem can be easily solved by several powerful numerical algorithms and solvers. With this approach, the robust stability of the overall system is ensured with a satisfactory system performance despite the presence of parametric uncertainties. Furthermore, experimental results clearly demonstrate the effectiveness of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Convex optimization

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Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

arXiv

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arXiv 2020年

作者： Graham, Simon Epstein, David Rajpoot, Nasir Department of Computer Science University of Warwick United Kingdom Mathematics for Real-World Systems Centre for Doctoral Training University of Warwick United Kingdom Mathematics Institute University of Warwick United Kingdom

—Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in data hungry models that learn independent features at each orientation. Allowing CNNs to be rotation-equivariant removes the necessity to learn this set of transformations from the data and instead frees up model capacity, allowing more discriminative features to be learned. This reduction in the number of required parameters also reduces the risk of overfitting. In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework. Each filter is defined as a linear combination of steerable basis filters, enabling exact rotation and decreasing the number of trainable parameters compared to standard filters. We also provide the first in-depth comparison of different rotation-equivariant CNNs for histology image analysis and demonstrate the advantage of encoding rotational symmetry into modern architectures. We show that DSF-CNNs achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology: breast tumour classification, colon gland segmentation and multi-tissue nuclear segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Rotation

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A Unified Decision-Theoretic Model for Information Gathering and Communication Planning

A Unified Decision-Theoretic Model for Information Gathering...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jennifer Renoux Tiago S. Veiga Pedro U. Lima Matthijs T. J. Spaan Center of Applied Autonomous Sensor Systems Orebro University Sweden Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Institute for Systems and Robotics Instituto Superior Tecnico University of Lisbon Portugal Department of Software Technology Delft University of Technology The Netherlands

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

We consider the problem of communication planning for human-machine cooperation in stochastic and partially observable environments. Partially Observable Markov Decision Processes with Information Rewards (POMDPs-IR) form a powerful framework for information-gathering tasks in such environments. We propose an extension of the POMDP-IR model, called a Communicating POMDP-IR (com-POMDP-IR), that allows an agent to proactively plan its communication actions by using an approximation of the human's beliefs. We experimentally demonstrate the capability of our com-POMDPIR agent to limit its communication to relevant information and its robustness to lost messages.

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Satellite-based InSAR Geodesy and Collocation with GNSS

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Procedia computer science 2024年 239卷 2329-2340页

作者： Juraj Papco Matus Bakon Lukas Kubica Gabika Belicova Branislav Droscak Martin Ferianc Martin Rovnak Antonio M. Ruiz Joaquim J. Sousa Department of Theoretical Geodesy and Geoinformatics Faculty of Civil Engineering Slovak University of Technology in Bratislava Radlinskeho 11 813 62 Bratislava Slovakia insar.sk Ltd Konstantinova 3 080 01 Presov Slovakia Department of Finance Accounting and Mathematical Methods Faculty of Management and Business University of Presov Konstantinova 16 080 01 Presov Slovakia Department of Engineering School of Sciences and Technology Universidade de Trás-os-Montes e Alto Douro Quinta de Prados 5000-801 Vila Real Portugal Geodetic and Cartographic Institute Bratislava Chlumeckeho 4 827 45 Bratislava Slovakia Department of Cartographic Geodetic and Photogrammetry Engineering University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Center for Advanced Studies in Earth Sciences Energy and Environment (CEACTEMA) University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Research Group RNM-282 Microgeodesia Jaén University of Jaén Campus Las Lagunillas s/n 23071 Jaén Spain Institute for Systems and Computer Engineering Technology and Science INESC-TEC 4200-465 Porto Portugal

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame.

关键词： InSAR geodesy collocation GNSS

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Human-collective visualization transparency

arXiv

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arXiv 2020年

作者： Roundtree, Karina A. Cody, Jason R. Leaf, Jennifer Onan Demirel, H. Adams, Julie A. School of Mechanical Industrial and Manufacturing Engineering Oregon State University Department of Electrical Engineering and Computer Science United States Military Academy Collaborative Robotics and Intelligent Systems Institute Oregon State University

Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. Human-collective transparency research has focused on how the design of the algorithms, visualizations, and control mechanisms influence human-collective behavior. Traditional collective visualizations have shown all of the individual entities composing a collective, which may become problematic as collectives scale in size and heterogeneity, and tasks become more demanding. Human operators can become overloaded with information, which will negatively affect their understanding of the collectives current state and overall behaviors, which can cause poor teaming performance. An analysis of visualization transparency and the derived visualization design guidance, based on remote supervision of collectives, are the primary contributions of this manuscript. The individual agent and abstract visualizations were analyzed for sequential best-of-n decision-making tasks involving four collectives, composed of 200 entities each. The abstract visualization provided better transparency by enabling operators with different individual differences and capabilities to perform relatively the same and promoted higher human-collective performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Transparency

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Correction to: SleepBoost: a multi‑level tree‑based ensemble model for automatic sleep stage classification

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Medical & biological engineering & computing 2024年第9期62卷 2785页

作者： Akib Zaman Shiu Kumar Swakkhar Shatabda Iman Dehzangi Alok Sharma Computer Science and Artificial Intelligence Laboratory (CSAIL) Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge MA USA. School of Electrical & Electronics Engineering Fiji National University Suva Fiji. shiu.kumar@fnu.ac.fj. Centre for Artificial Intelligence and Robotics (CAIR) United International University Dhaka Bangladesh. Department of Computer Science Rutgers University Camden NJ USA. Center for Computational and Integrative Biology Rutgers University Camden USA. Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230‑0045 Japan. Institute for Integrated and Intelligent Systems Griffith University Nathan Brisbane QLD Australia.

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One to many: Adaptive instrument segmentation via meta learning and dynamic online adaptation in robotic surgical video

arXiv

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arXiv 2021年

作者： Zhao, Zixu Jin, Yueming Lu, Bo Ng, Chi-Fai Dou, Qi Liu, Yun-Hui Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China The Department of Surgery The Chinese University of Hong Kong Hong Kong

Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampled from the same domain. However, it is impractical and expensive to collect and annotate sufficient data from every new domain. To greatly increase the label efficiency, we explore a new problem, i.e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame. We propose MDAL, a meta-learning based dynamic online adaptive learning scheme with a two-stage framework to fast adapt the model parameters on the first frame and partial subsequent frames while predicting the results. MDAL learns the general knowledge of instruments and the fast adaptation ability through the video-specific meta-learning paradigm. The added gradient gate excludes the noisy supervision from pseudo masks for dynamic online adaptation on target videos. We demonstrate empirically that MDAL outperforms other state-of-the-art methods on two datasets (including a real-world RAS dataset). The promising performance on ex-vivo scenes also benefits the downstream tasks such as robot-assisted suturing and camera control. Copyright © 2021, The Authors. All rights reserved.

关键词： Surgical equipment

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Overcoming Data Scarcity in Speaker Identification: Dataset Augmentation with Synthetic MFCCs via Character-level RNN

Overcoming Data Scarcity in Speaker Identification: Dataset ...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Jordan J. Bird Diego R. Faria Cristiano Premebida Anikó Ekárt Pedro P. S. Ayrosa ARVIS Lab Aston University Birmingham UK Institute of Systems and Robotics University of Coimbra Coimbra Portugal School of Engineering and Applied Science Aston University Birmingham UK Department of Computer Science Universidade Estadual de Londrina Londrina Brazil

ISBN: (数字)9781728170787

ISBN: (纸本)9781728170794

Autonomous speaker identification suffers issues of data scarcity due to it being unrealistic to gather hours of speaker audio to form a dataset, which inevitably leads to class imbalance in comparison to the large data availability from non-speakers since large-scale speech datasets are available online. In this study, we explore the possibility of improving speaker recognition by augmenting the dataset with synthetic data produced by training a Character-level Recurrent Neural Network on a short clip of five spoken sentences. A deep neural network is trained on a selection of the Flickr8k dataset as well as the real and synthetic speaker data (all in the form of MFCCs) as a binary classification problem in order to discern the speaker from the Flickr speakers. Ranging from 2,500 to 10,000 synthetic data objects, the network weights are then transferred to the original dataset of only Flickr8k and the real speaker data, in order to discern whether useful rules can be learnt from the synthetic data. Results for all three subjects show that fine-tune learning from datasets augmented with synthetic speech improve the classification accuracy, F1 score, precision, and the recall when applied to the scarce real data vs non-speaker data. We conclude that even with just five spoken short sentences, data augmentation via synthetic speech data generated by a Char- RNN can improve the speaker classification process. Accuracy and related metrics are shown to improve from around 93% to 99% for three subjects classified from thousands of others when fine-tuning from exposure to 2500-1000 synthetic data points. High F1 scores, precision and recall also show that issues due to class imbalance are also solved.

关键词： Data models Mel frequency cepstral coefficient Recurrent neural networks Speech recognition Speaker recognition Human-robot interaction Autonomous systems

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Rota-Net: Rotation Equivariant Network for Simultaneous Gland and Lumen Segmentation in Colon Histology Images 15th

Rota-Net: Rotation Equivariant Network for Simultaneous Glan...

引用

15th European Congress on Digital Pathology, ECDP 2019

作者： Graham, Simon Epstein, David Rajpoot, Nasir Mathematics for Real-World Systems Centre for Doctoral Training University of Warwick Coventry United Kingdom Department of Computer Science University of Warwick Coventry United Kingdom Mathematics Institute University of Warwick Coventry United Kingdom

ISBN: (纸本)9783030239367

Analysis of the shape of glands and their lumen in digitised images of Haematoxylin & Eosin stained colon histology slides can provide insight into the degree of malignancy. Segmenting each glandular component is an essential prerequisite step for subsequent automatic morphological analysis. Current automated segmentation approaches typically do not take into account the inherent rotational symmetry within histology images. We incorporate this rotational symmetry into an encoder-decoder based network by utilising group equivariant convolutions, specifically using the symmetry group of rotations by multiples of 90 Our rotation equivariant network splits into two separate branches after the final up-sampling operation, where the output of a given branch achieves either gland or lumen segmentation. In addition, at the output of the gland branch, we use a multi-class strategy to assist with the separation of touching instances. We show that our proposed approach achieves the state-of-the-art performance on the GlaS challenge dataset. © 2019, Springer Nature Switzerland AG.

关键词： Rotation

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Variable Stiffness Control with Strict Frequency Domain Constraints for Physical Human-Robot Interaction

arXiv

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arXiv 2020年

作者： Zou, Wulin Duan, Pu Chen, Yawen Yu, Ningbo Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Xeno Dynamics Co. Ltd Shenzhen China Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method. Copyright © 2020, The Authors. All rights reserved.

关键词： Frequency domain analysis

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