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arXiv 2019年

作者： Zhu, Yabin Li, Chenglong Luo, Bin Tang, Jin Wang, Xiao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei Anhui China

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

Robotic Electrospinning Actuated by Non-Circular Joint Conti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zicong Wu Chuqian Lou Zhu Jin Shaoping Huang Ning Liu Yun Zou Mirko Kovac Anzhu Gao Guang-Zhong Yang Shanghai Jiao Tong University Shanghai P. R. China Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology Precision Robotics (Hong Kong) Limited Hong Kong China Aerial Robotics Laboratory Imperial College London London United Kingdom Shanghai Jiao Tong University and the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai P. R. China

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments.

关键词： Drugs Electric potential Fluids Tissue engineering Medical treatment Bending Manipulators

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Structural Performance Analysis of Wall Climbing Robot 2

Structural Performance Analysis of Wall Climbing Robot

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2019 2nd International Conference on Advanced Materials, intelligent Manufacturing and Automation, AMIMA 2019

作者： Huang, Li Liao, Shangchun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan Hubei430081 China

With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot. © Published under licence by IOP Publishing Ltd.

关键词： Robot applications

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Infrared Dim and Small Target Detection and Tracking Based on Single Multi-Frame Algorithm under Sea Clutter Background

Infrared Dim and Small Target Detection and Tracking Based o...

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第三十八届中国控制会议

作者： Ting Zhao Taoshen Wang Yaoxin Cao Yunze Cai Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai Jiao Tong University Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai Jiao Tong University Shanghai Institute of Spaceflight Control Technology

Infrared dim and small target detection and track under sea clutter is significant and crucial in the infrared image process. It is a difficult task because of the disturbing clutter, the changing scenario and weak targets. In this paper, a complete process to detect and track dim small targets is proposed. Firstly, we use Mean and 3 D Filtering with Block-Matching(M3 DBM)algorithm to denoise infrared images. Inspired by the well-known BM3 D algorithm and NLM algorithm, M3 DBM can keep small targets from impair while denoising. Then, we use Butterworth high-pass filter and morphological filter to extract possible targets from single frame image. Finally, an optimal pipeline filtering algorithm(OPF) based on target position prediction and multi-target stable track is proposed. Optimal means that choose the best pipe for target and vice versa. OPF can track targets stably and accurately. The experimental results show that the proposed algorithm achieves good results in both accuracy and efficiency.

关键词： Infrared dim and small targets Sea clutter background M3DBM denoising Butterworth high-pass morphological Optimal pipeline filtering

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Multi-Adapter RGBT Tracking

Multi-Adapter RGBT Tracking

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Cheng Long Li Andong Lu Ai Hua Zheng Zhengzheng Tu Jin Tang Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-of-the-art RGB and RGBT tracking algorithms.

关键词： Adaptation models Robustness Target tracking Feature extraction Visualization Task analysis

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

arXiv

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arXiv 2021年

作者： Wu, Zicong Lou, Chuqian Jin, Zhu Huang, Shaoping Liu, Ning Zou, Yun Kovac, Mirko Gao, Anzhu Yang, Guang-Zhong Institute of Medical Robotics Department of Bioengineering Shanghai Jiao Tong University Shanghai200240 China Imperial College London Empa Swiss Federal Laboratories for Materials Science and Technology Limited Hong Kong999077 Hong Kong Aerial Robotics Laboratory Imperial College London LondonSW7 2AZ United Kingdom Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

关键词： Electrospinning

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Multi-task deep learning with dynamic programming for embryo early development stage classification from time-lapse videos

arXiv

引用

arXiv 2019年

作者： Liu, Zihan Huang, Bo Cui, Yuqi Xu, Yifan Zhang, Bo Zhu, Lixia Wang, Yang Jin, Lei Wu, Dongrui Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Reproductive Medicine Center Huazhong University of Science and Technology Tongji Hospital Wuhan430074 China

Time-lapse is a technology used to record the development of embryos during in-vitro fertilization (IVF). Accurate classification of embryo early development stages can provide embryologists valuable information for assessing the embryo quality, and hence is critical to the success of IVF. This paper proposes a multi-task deep learning with dynamic programming (MTDL-DP) approach for this purpose. It first uses MTDL to pre-classify each frame in the time-lapse video to an embryo development stage, and then DP to optimize the stage sequence so that the stage number is monotonically non-decreasing, which usually holds in practice. Different MTDL frameworks, e.g., oneto-many, many-to-one, and many-to-many, are investigated. It is shown that the one-to-many MTDL framework achieved the best compromise between performance and computational cost. To our knowledge, this is the first study that applies MTDL to embryo early development stage classification from time-lapse videos. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Event-Triggered Model Predictive control for A Three-Phase Inverter with Output LC Filter

Event-Triggered Model Predictive Control for A Three-Phase I...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Benfei WANG Jingjing HUANG Jose RODRIGUEZ Cristian GARCIA Guodong FENG School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Engineering School Universidad Andrés Bello Santiago Chile Engineering Faculty Universidad de Talca Curico Chile

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

This paper proposes an event-triggered model predictive control (ET-MPC) method for the three-phase inverter with output LC filter. The proposed method is developed by combining the conventional finite-control-set MPC (FCS-MPC) method and the ET control strategy. The ET control strategy incorporates a preset triggering condition based on the requirement of the regulation performance for the three-phase inverter. When the state of the three-phase inverter activates the triggering condition, FCS-MPC is triggered with the updated state so that the optimal control actions can be generated when it is necessary. If the state of the inverter is within the preset triggering condition, FCS-MPC is suspended and the control actions are held as constant. Consequently, the unnecessary online prediction, online optimization, and the corresponding control actions can be avoided. Therefore, with the tolerate regulation performance, the proposed ET-MPC can reduce the computational burden of the conventional FCS-MPC. The various simulation studies of the three-phase inverter are conducted and discussed to demonstrate the effectiveness of the proposed ET-MPC method.

关键词： Computational modeling Simulation Switches Inverters Regulation Optimization Predictive control

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Refrigerant capacity detection of dehumidifier based on time series and neural networks 1

引用

13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Peng, Gang Yang, Zuhuang Wang, Min School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan China Shenzhen Institute Huazhong University of Science and Technology Shenzhen518060 China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

Refrigerant leakage is common in the use of dehumidifier, leading to decrease of dehumidification ability. To detect refrigerant capacity effectively and maintain it timely, a neural network with deep learning skills was proposed based on time series. As input of networks, the time series consists of the operating parameters of the dehumidifier at multiple time points. To determine the refrigerant capacity, the proposed method combines the outputs on the neural networks of all the time series examples in a single run of dehumidifier. As the result suggests, the proposed method is capable of detecting the refrigerant capacity with a low missing rate and high accuracy, which improves the maintenance mechanism of the dehumidifier and is of great significance to ensuring a comfortable air environment for user. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Time series

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