检索结果-内蒙古大学图书馆

Towards better surgical instrument segmentation in endoscopic vision: multi-angle feature aggregation and contour supervision

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Qin, Fangbo Lin, Shan Li, Yangming Bly, Randall A. Moe, Kris S. Hannaford, Blake Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China SeattleWA98195-2500 United States Department of Otolaryngology-Head & Neck Surgery UW Seattle United States Department of Electrical Computer and Telecommunications Engineering Technology Rochester Institute of Technology United States

—Accurate and real-time surgical instrument segmentation is important in the endoscopic vision of robot-assisted surgery, and significant challenges are posed by frequent instrument-tissue contacts and continuous change of observation perspective. For these challenging tasks more and more deep neural networks (DNN) models are designed in recent years. We are motivated to propose a general embeddable approach to improve these current DNN segmentation models without increasing the model parameter number. Firstly, observing the limited rotation-invariance performance of DNN, we proposed the Multi-Angle Feature Aggregation (MAFA) method, leveraging active image rotation to gain richer visual cues and make the prediction more robust to instrument orientation changes. Secondly, in the end-to-end training stage, the auxiliary contour supervision is utilized to guide the model to learn the boundary awareness, so that the contour shape of segmentation mask is more precise. The effectiveness of the proposed methods is validated with ablation experiments conducted on novel Sinus-Surgery datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Silicone Oil Damping for Quasi-static Micro Scanners with Electrostatic Staggered Vertical Comb Drives

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2019年第15期52卷 37-42页

作者： Richard Schroedter Jan Grahmann Klaus Janschek Fraunhofer Institute for Photonic Microsystems Active Microscanner Systems Maria-Reiche-Str.2 01109 Dresden Germany Automation and Control Institute (ACIN) Faculty of Electrical Engineering and Information Technology TU Wien Gußhausstraße 27-29 1040 Wien Austria Institute of Automation Faculty of Electrical and Computer Engineering Technische Universität Dresden 01062 Dresden Germany

Many applications like image projection, distance sensors or spectroscopy require high speed quasi-static operating scanners with the size of several millimeters for the laser beam guidance. Quasi-static micro scanners with electrostatic staggered vertical comb drives of sizes up to 5 mm achieve remarkable mechanical deflections of ±8 degree and eigenfrequencies of several hundred Hertz. Closed-loop control of the scanner position highly reduces parasitic eigenfrequency oscillation when driving an arbitrary trajectory like triangle or saw tooth, but requires position feedback and high performant drive electronics for real-time control. In this contribution we investigate silicone oil to attenuate undesired oscillations by passive mechanic damping. We present experimental results with different viscosity and determine the resulting system parameters such as damping coefficient, eigenfrequency and permittivity, supported by transient simulations. While the permittivity, refractive index and damping increase significantly, the deceasing eigenfrequency results in slower admissible trajectories. The overall performance is assessed and compared to the active damping approach using closed-loop control of the micro scanner operating in air. Finally, a potential packaging technology is presented.

关键词： laser beam guidance quasi-static micro scanner electrostatic staggered vertical comb drive silicone oil viscous passive damping permittivity close-loop trajectory control

Synaptic learning with augmented spikes

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Yu, Qiang Song, Shiming Ma, Chenxiang Pan, Linqiang Tan, Kay Chen Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Image Information Processing and Intelligent Control Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The Department of Computer Science City University of Hong Kong Hong Kong

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this paper, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insight into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness and applicability to practical tasks such as acoustic and visual pattern recognition. The remarkable results highlight the effectiveness and potential merits of our methods. Importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them including neuromorphic computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

Multi-dispatch for Grid-connected Microgrid with Robust Optimization Algorithm

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2018年第28期51卷 474-479页

作者： Shi, Ruifeng Li, Shaopeng Sun, Changhao Lee, Kwang Y. School of Control and Computer Engineering North China Electric Power University Beijing China Smart Energy Institute Nanjing SAC Power Grid Automation Co. Ltd Nanjing211100 China Department of Electrical and Computer Engineering Baylor University TX76798 United States

Increasing penetration of uncoordinated electric vehicles (EVs) and intermittent renewable energy generation in microgrid systems motivates us to explore an effective strategy for the safe and economic operation of such distributed generation (DG) systems. This paper presents a robust economic dispatch strategy for grid-connected microgrids. Uncertainty from wind power and EV charging loads is considered as an uncertain set of interval predictions. Considering the worst application scenario, the proposed strategy can help to schedule the EV charging behaviors and DG outputs in order to reduce operation cost under practical constraints. In addition, in order to facilitate the decision makers to make decision strategies from the perspective of economy, this paper explores the relationship between the volatility of uncertain parameters and economy based on the theory of interval forecast. Numerical case study shows the feasibility of the proposed dispatch strategy. © 2018

关键词： Electric load dispatching Decision making Decision theory Economic analysis Electric vehicles Optimization Scheduling Uncertainty analysis Wind power Distributed generation system Electric Vehicles (EVs) Ev charging behaviors Micro grid Multi Dispatch Renewable energy generation Robust optimization Robust optimization algorithm

From AR to AI: Augmentation Technology for Intelligent Surgery and Medical Treatments

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2020年第5期53卷 792-796页

作者： Mei Zhang Zhicheng Zhang Xiao Wang Hui Yu Yifan Xia Kanran Tan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China and also with the Qingdao Academy of Intelligent Industries Qingdao 266109 China The Department of the 7th Medical Center of PLA General Hospital Beijing 100700 China The Department of Computer Science Purdue University West Lafayette Indiana 47906 USA The School of Creative Technologies University of Portsmouth UK The Department of Computer Science Johns Hopkins University Baltimore Maryland USA

With the development of augmented reality (AR) technologies, more and more approaches are proposed for medical applications. With the help of AR technology, the doctor can highly improve the spatial perception and obtain more information beneath the displayed image. Generally, 3D reconstruction, registration, tracking and depth visualization are all important for an AR system, which directly determine the accuracy of the system. However, none of recent systems have achieved a perfect 3D vision for doctors. It is particularly challenging for the AR system to perform well for operation on highly deformable body tissues. Therefore, there is an urgent need to improve the 3D vision and intelligence of AR systems. In this paper, we provide a review of the recent development of augmentation technologies for surgery and medical treatment. And then, we introduce the parallel intelligence theory into AR systems, which can provide a feasible approach to enhance the efficiency.

关键词： Potential impact of automation open problems Augmented reality Medical treatment Parallel theory 3D technology

Ultimate Boundedness control for Networked Systems with Redundant Channel under Quantization Effects

学校读者我要写书评

暂无评论

Ultimate Boundedness Control for Networked Systems with Redu...

第三十八届中国控制会议

作者： Zhongyi Zhao Zidong Wang Lei Zou Hongli Dong College of Electrical Engineering and Automation Shandong University of Science and Technology Department of Computer Science Brunel University London Institute of Complex Systems and Advanced Control Northeast Petroleum University

This paper investigates the ultimate boundedness control problem for a class of networked systems with redundant channels under uniform quantization effects. Quantized output of concerned NCSs is transmitted to remote controller through a communication channel. For the purpose of improving communication quality, a redundant channel connected with a more precise quantizer is also employed, where the time to accomplish quantizing task is also considered. The transmission delays of such a redundant channel are modelled by a sequence of independent and identically distributed(i.i.d.) stochastic variable. An output feedback controller is developed under which the exponentially ultimate boundedness in mean square could be *** the virtue of stochastic analysis technology and Lyapunov-like method, sufficient conditions are established to ensure the existence of the desired controller, and the desired gain matrices are then obtained by solving a set of linear matrix ***, the effectiveness of designed controller is demonstrated through a simulation example.

关键词： Networked systems uniform quantization redundant channel ultimate boundedness control linear matrix inequal ities

An Adaptive Strategy via Reinforcement Learning for the Prisoner＇s Dilemma Game

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 301-310页

作者： Lei Xue Changyin Sun Donald Wunsch Yingjiang Zhou Fang Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO 65409 USA College of Automation Nanjing University of Posts and Telecommunications Nanjing 210023 China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai 210096 China IEEE

The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy.

关键词： Complex network prisoner’s dilemma reinforcement learning temporal differences learning

Modal regression based structured low-rank matrix recovery for multi-view learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

Multi-view adaptive fusion network for 3D object detection

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wang, Guojun Tian, Bin Zhang, Yachen Chen, Long Cao, Dongpu Wu, Jian State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China School of Data and Computer Science Sun Yat-Sen University Guangzhou510275 China Lab University of Waterloo Canada College of Automotive Engineering Jilin University China

3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse both modalities without information loss and interference. To solve this issue, we propose a single-stage multi-view fusion framework that takes LiDAR Birds-Eye View, LiDAR Range View and Camera View images as inputs for 3D object detection. To effectively fuse multi-view features, we propose an Attentive Pointwise Fusion (APF) module to estimate the importance of the three sources with attention mechanisms which can achieve adaptive fusion of multi-view features in a pointwise manner. Besides, an Attentive Pointwise Weighting (APW) module is designed to help the network learn structure information and point feature importance with two extra tasks: foreground classification and center regression, and the predicted foreground probability will be used to reweight the point features. We design an end-to-end learnable network named MVAF-Net to integrate these two components. Our evaluations conducted on the KITTI 3D object detection datasets demonstrate that the proposed APF and APW module offer significant performance gain and that the proposed MVAF-Net achieves state-of-the-art performance in the KITTI benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition