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Semi-supervised medical image classification with relation-driven self-ensembling model

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arXiv 2020年

作者： Liu, Quande Yu, Lequan Luo, Luyang Dou, Qi Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Department of Radiation Oncology Stanford University StanfordCA94305 United States T Stone Robotics Institute Chinese University of Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. In this paper, we present a novel relation-driven semi-supervised framework for medical image classification. It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data. Considering that human diagnosis often refers to previous analogous cases to make reliable decisions, we introduce a novel sample relation consistency (SRC) paradigm to effectively exploit unlabeled data by modeling the relationship information among different samples. Superior to existing consistency-based methods which simply enforce consistency of individual predictions, our framework explicitly enforces the consistency of semantic relation among different samples under perturbations, encouraging the model to explore extra semantic information from unlabeled data. We have conducted extensive experiments to evaluate our method on two public benchmark medical image classification datasets, i.e., skin lesion diagnosis with ISIC 2018 challenge and thorax disease classification with ChestX-ray14. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

Attitude observers aided by implicit measurements of the Earth angular velocity

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Attitude observers aided by implicit measurements of the Ear...

IEEE Conference on Decision and Control

作者： Joel Reis Carlos Silvestre Pedro Batista Paulo Oliveira Faculty of Science and Technology University of Macau Taipa Macao China Instituto Superior Técnico Universidade de Lisboa Portugal Institute for Systems and Robotics LARSyS Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal LAETA — Associated Laboratory for Energy Transports and Aeronautics IDMEC — Institute of Mechanical Engineering Instituto Superior Técnico Universidade deLisboa Lisboa Portugal

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this tutorial, a new class of attitude estimation solutions is presented to determine the orientation of a robotic platform resorting only to a set of triaxial gyroscopes and an additional measurement of a constant inertial vector. This approach poses a significant advantage in terms of setup design and reduced mission costs since only one inertial measurement is required, in contrast with existing solutions that require at least two of these. Pertinent examples of viable applications include navigation of submarines or large ships, assisting autonomous robots in precise maneuvers, inertial platform stabilization, etc. The novelty of the solutions proposed herein exploit the well-known inertial information about the Earth's angular velocity by using high-grade gyroscopes that are sensitive to the planet's spin. Experimental results are presented to demonstrate the good performance achieved by the algorithms.

关键词： Observers Extraterrestrial measurements Earth Robot sensing systems Angular velocity Tutorials Gyroscopes

A spatial motion control to transfer an object between a pair of air jet 15

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A spatial motion control to transfer an object between a pai...

15th International Conference on Informatics in Control, Automation and robotics, ICINCO 2018

作者： Yoshinaga, Kazuki Iwaki, Satoshi Tsuchihashi, Naoki Abe, Nobukado Ikeda, Tetsushi Kosaku, Toshiharu Takaki, Takeshi Systems Engineering Robotics Laboratory Graduate School of Information Science and Technology Hiroshima City University Asaminami Hiroshima Hiroshima Prefecture Japan Hiroshima University Graduate school of Engineering Hiroshima Japan

ISBN: (纸本)9789897583216

We propose a method in which multiple nozzles are arranged consecutively along a conveying line and an object can be relayed one after another. In this paper, as a most essential technology for such a conveyor, we focus on the transferring control of the object between a pair of air jet on a vertical plane. We propose a relaying control method based on some natural behaviours of an object in a fluid field where two air jet streams collide. Some successful experiment results are shown. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： Conveying

Quantum tomography by regularized linear regression

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arXiv 2019年

作者： Mu, Biqiang Qi, Hongsheng Petersen, Ian R. Shi, Guodong Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Research School of Electrical Energy and Materials Engineering Australian National University Canberra0200 Australia Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering University of Sydney 2008 Australia

In this paper, we study extended linear regression approaches for quantum state tomography based on regularization techniques. For unknown quantum states represented by density matrices, performing measurements under certain basis yields random outcomes, from which a classical linear regression model can be established. First of all, for complete or over-complete measurement bases, we show that the empirical data can be utilized for the construction of a weighted least squares estimate (LSE) for quantum tomography. Taking into consideration the trace-one condition, a constrained weighted LSE can be explicitly computed, being the optimal unbiased estimation among all linear estimators. Next, for general measurement bases, we show that '2-regularization with proper regularization gain provides even lower mean-square error under a cost in bias. The regularization parameter is tuned by two estimators in terms of a risk characterization. Finally, a concise and unified formula is established for the regularization parameter with complete measurement basis under an equivalent regression model, which proves that the proposed tuning estimators are asymptotically optimal as the number of samples grows to infinity under the risk metric. Additionally, numerical examples are provided to validate the established results. Copyright © 2019, The Authors. All rights reserved.

关键词： Tomography

A registration-aided domain adaptation network for 3D Point cloud based place recognition

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Measurement-induced boolean dynamics and controllability for quantum networks

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arXiv 2019年

作者： Qi, Hongsheng Muy, Biqiang Petersenz, Ian R. Shix, Guodong Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Research School of Electrical Energy and Materials Engineering Australian National University Canberra0200 Australia Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering University of Sydney NSW2008 Australia

In this paper, we study dynamical quantum networks which evolve according to Schrodinger equations but subject to sequential local or global quantum measurements. A network of qubits forms a composite quantum system whose state undergoes unitary evolution in between periodic measurements, leading to hybrid quantum dynamics with random jumps at discrete time instances along a continuous orbit. The measurements either act on the entire network of qubits, or only a subset of qubits. First of all, we reveal that this type of hybrid quantum dynamics induces probabilistic Boolean recursions representing the measurement outcomes. With global measurements, it is shown that such resulting Boolean recursions define Markov chains whose state-transitions are fully determined by the network Hamiltonian and the measurement observables. Particularly, we establish an explicit and algebraic representation of the underlying recursive random mapping driving such induced Markov chains. Next, with local measurements, the resulting probabilistic Boolean dynamics is shown to be no longer Markovian. The state transition probability at any given time becomes dependent on the entire history of the sample path, for which we establish a recursive way of computing such non-Markovian probability transitions. Finally, we adopt the classical bilinear control model for the continuous Schrodinger evolution, and show how the measurements affect the controllability of the quantum networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics

Measurement-Induced boolean dynamics for open quantum networks

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arXiv 2019年

作者： Qi, Hongsheng Mu, Biqiang Petersen, Ian R. Shi, Guodong Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Research School of Electrical Energy and Materials Engineering Australian National University Canberra0200 Australia Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering University of Sydney NSW2008 Australia

In this paper, we study the recursion of measurement outcomes for open quantum networks under sequential measurements. Open quantum networks are networked quantum subsystems (e.g., qubits) with the state evolutions described by a continuous Lindblad master equation. When measurements are performed sequentially along such continuous dynamics, the quantum network states undergo random jumps and the corresponding measurement outcomes can be described by a vector of probabilistic Boolean variables. The induced recursion of the Boolean vectors forms a probabilistic Boolean network. First of all, we show that the state transition of the induced Boolean networks can be explicitly represented through realification of the master equation. Next, when the open quantum dynamics is relaxing in the sense that it possesses a unique equilibrium as a global attractor, structural properties including absorbing states, reducibility, and periodicity for the induced Boolean network are direct consequences of the relaxing property. Particularly, we show that generically, relaxing quantum dynamics leads to irreducible and aperiodic chains for the measurement outcomes. Finally, we show that for quantum consensus networks as a type of non-relaxing open quantum network dynamics, the communication classes of the measurement-induced Boolean networks are encoded in the quantum Laplacian of the underlying interaction graph. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

Recursive LMMSE Centralized Fusion with Compressed Multi-Radar Measurements

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Recursive LMMSE Centralized Fusion with Compressed Multi-Rad...

International Conference on Control, Automation and Information sciences (ICCAIS)

作者： Qingpeng Zhang Zhansheng Duan Uwe D. Hanebeck Center for Information Engineering Science Research Xi'an Jiaotong University Xi'an Shaanxi China Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9781728123110

ISBN: (纸本)9781728123127

For multi-sensor centralized fusion with linear measurements, simply stacking all measurements up and then applying the Kalman filter at the fusion center can give the optimal estimation performance. This optimal performance is independent of how the measurements from different sensors are stacked up. However, for centralized fusion with multiple radar measurements under the recursive LMMSE filtering framework, the performance really matters as to how to stack the measurements from different radars. In [1], we have shown that centralized fusion with stacked recombined multi-radar measurements outperforms the one with stacked original measurements under the recursive LMMSE filtering framework. In this paper, we further develop a new multi-radar centralized fusion approach by compressing all measurements first and then applying the recursive LMMSE filter with single radar measurements at the fusion center. Numerical examples show that the new centralized fusion with compressed measurements has better estimation accuracy and smaller noncredibility than the ones with stacked measurements.

关键词： Radar measurements Coordinate measuring machines Noise measurement Kalman filters Standards Estimation

Motion control of nanomanipulation platform based on feedforward compensation inside SEM

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Motion control of nanomanipulation platform based on feedfor...

IEEE International Conference on Nano/Micro Engineered and Molecular systems

作者： Mingyu Wang Lining Sun Zhan Yang Toshio Fukuda Tao Chen Provincial Jiangsu Key Laboratory for Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology soochow university Suzhou China Department of Micro-Nano Systems Engineering Nagoya University Japan

With the rapid development and wide application of nanotechnology, nanodevice manufacturing with nanotechnology as its core has become one of the key research areas in nanotechnology. Micro-nano operating system based on scanning electron microscope (SEM) is one of the methods to realize nanodevice manufacturing. SmarAct nanomanipulation platform is an important component of micro-nano operating system, which can realize linear motion in three-dimensional space. However, due to mechanical structure and assembly errors, the SmarAct nanomanipulation platform cannot meet the stable requirements during motion. In this paper, the visual feedback and image processing are used to describe the motion trajectory of SmarAct operator. Based on the motion mechanism of SmarAct operator, the error source is analyzed, at the same time, the motion error is modeled according to the motion trajectory. The feedforward compensation is used to realize the stable linear motion of SmarAct operator, and the interpolation algorithm is used to realize the trajectory motion control of the SmarAct nanomanipulation platform.

关键词： Trajectory Scanning electron microscopy Operating systems Carbon nanotubes Interpolation Real-time systems