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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Siyu Li Zhen Li Limin Guo Gui-Bin Bian Beijing University of Technology Beijing PR China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Clinical research shows that glaucoma is primarily caused by pathological changes in the optic nerve structure, which may bring about irreversible damage of sight. In relevant literature research, the cup-to-disc ratio (CDR) is mainly used as an important indicator for glaucoma detection, which needs to segment optic disc (OD) and optic cup (OC) region clearly and accurately. However, due to the low contrast image of boundary, the segmentation of OD and OC is still a challenging problem. In this paper, a novel hybrid model based on the ensemble random-forest deep-neural-network (RF-DNN) is proposed for OD and OC segmentation, which can calculate more accurately for glaucoma detection. At the same time, the advantages of DNN and ensemble RF are combined to carry out the corresponding feature extraction and classification, which employs the winner-takes-all strategically to the segmentation and classification for glaucoma detection. Finally, the experiment result shows that the proposed method has reached the best evaluation level in terms of OD and OC segmentation results on ORIGA dataset and SCES dataset, which achieves highest diagnosis accuracy with AUC of 0.96 and 0.98 on ORIGA dataset and SCES dataset, respectively.

关键词： Feature extraction Image segmentation Training Optical imaging Decoding Neural networks Robots

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Device Placement Optimization for Deep Neural Networks via One-shot Model and Reinforcement Learning

Device Placement Optimization for Deep Neural Networks via O...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zixiang Ding Yaran Chen Nannan Li Dongbin Zhao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

With the development of deep learning that employs deep neural networks (DNN) as powerful tool, its computational requirement grows rapid together with the increasing size (e.g. depth and parameter) of DNN. Currently, model and data parallelism are employed for accelerating the training and inference process of DNN. However, the above techniques make placement decision on devices for DNN based on heuristics and intuitions by machine learning experts. In this paper, we propose an novel approach for designing device placement of DNN in an automatic way. For a DNN, we employ a sequence-to-sequence model as controller to sample device placement from a one-shot model, which contains all possible device placements with respect to a specific hardware environment (e.g. CPU and GPU). Then, reinforcement learning treats the execution time of sampled device placement as reward to guide the sequence-to-sequence model for finding better one. The proposed approach is employed to optimize the device placement for both model and data parallelism of Inception-V3 on ImageNet. Experimental results show that the optimal placements discovered by our method can outperform hand-crafted one.

关键词： Parallel processing Data models Computational modeling Optimization Reinforcement learning Hardware Training

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DeepFatigueNet: A model for automatic visual fatigue assessment based on raw single-channel EEG

DeepFatigueNet: A model for automatic visual fatigue assessm...

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SID Symposium, Seminar, and Exhibition 2019, Display Week 2019

作者： Song, Yaguang Wang, Danli Yue, Kang Zheng, Nan The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781510896161

Three-dimension (3D) display has become increasingly popular in many fields. However, watching 3D content continuously can lead to visual fatigue that is harmful to users’ vision system. Visual fatigue assessment aims at monitoring users’ brain states based on the electroencephalogram (EEG) signals to identify different fatigue levels and avoid severe fatigue. Most of existing studies on the modeling of visual fatigue assessment rely on manual features extracted from EEG, which is time-consuming and needs prior knowledge. Convolutional Neural Networks (CNNs) which have been used in computer vision, speech recognition have attracted increasing interest. There is still a lack of attempts to employ end- to-end EEG analysis on visual fatigue assessment In this paper, we propose a deep learning model DeepFatigueNet to perform automatic feature extraction and classification from raw single-channel EEG. The DeepFatigueNet is evaluated on our own visual fatigue dataset and compared with the state-of-the-art deep learning methods for EEG-based tasks. The overall accuracy of DeepFatigueNet reaches 75.9% on the three-classification task exceeding other models. The experimental results demonstrate the effectiveness of our model and show the potential of deep convolutional neural networks for end-to-end visual fatigue assessment. © 2019 SID-the Society for Information Display. All rights reserved.

关键词： Electroencephalography

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DGST: Discriminator Guided Scene Text detector

arXiv

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arXiv 2020年

作者： Zhao, Jinyuan Wang, Yanna Xiao, Baihua Shi, Cunzhao Jia, Fuxi Wang, Chunheng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences China

Scene text detection task has attracted considerable attention in computer vision because of its wide application. In recent years, many researchers have introduced methods of semantic segmentation into the task of scene text detection, and achieved promising results. This paper proposes a detector framework based on the conditional generative adversarial networks to improve the segmentation effect of scene text detection, called DGST (Discriminator Guided Scene Text detector). Instead of binary text score maps generated by some existing semantic segmentation based methods, we generate a multi-scale soft text score map with more information to represent the text position more reasonably, and solve the problem of text pixel adhesion in the process of text extraction. Experiments on standard datasets demonstrate that the proposed DGST brings noticeable gain and outperforms state-of-the-art methods. Specifically, it achieves an F-measure of 87% on ICDAR 2015 dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Design and validation of an asymmetric bowden-cable-driven series elastic actuator

Design and validation of an asymmetric bowden-cable-driven s...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Sun, Ning Cheng, Long Tian, Lin Hou, Zeng-Guang Tan, Min Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

Weight, size and torque control for hand exoskeleton robots are challenging due to lack of small and compact bidirectional torque actuators. In this paper, an asymmetric Bowden-cable-driven series elastic actuator (ABLE-SEA) for hand exoskeleton robots was proposed based on the fact that the required torques for finger movements are asymmetric. Compared to series elastic actuator and transmission system that were designed separately, the elastic elements of ABLESEA were placed in transmission parts to connect series elastic actuator with transmission system, which makes system compact. ABLE-SEA is 71 mm × 19.5mm × 20mm in dimension and weighs 30g except motor which is placed remotely. The dynamic model of ABLE-SEA was established, and feedback proportional-derivative (PD) control plus a feed-forward term was used to track the reference torque for the ABLE-SEA. The reference torque tracking test at different frequencies was performed at the developed prototype. Meanwhile, the peak torque of ABLE-SEA was tested. The experimental results verified that the torque bandwidth of the proposed series elastic actuator could reach 4Hz, and the peak torque of ABLE-SEA could reach 0.3Nm, which meet the design requirement. © 2019 IEEE.

关键词： Torque

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Design of a robotic fish based on a passive flexible mechanism

Design of a robotic fish based on a passive flexible mechani...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Lu, Ben Fu, Yuzhuo Zou, Qianqian Deng, Sai Zhou, Chao Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728163215

In recent years, along with the rapid development of manufacturing, control, sensing and other technologies, biomimetic robotic fish has played an increasingly important role in marine exploration, underwater rescue, water quality monitoring and other fields. This paper designed a robotic fish based on a passive flexible mechanism to realize the fish-like propulsion function. The motion simulation of the designed transmission mechanism was carried out to get the actual curve of the structure. Through the analysis of the force of the fishtail, the preliminary selection scheme of the elastic joints based on the passive flexible mechanism was obtained. Finally, this paper built a robotic fish based on a passive flexible mechanism, which could achieve the motion frequency adjustable from 0.5 to 9 Hz, and the maximum swimming speed reaching 1.05 BL/s (BL, body length). This design scheme provided a new reference for the design of the biomimetic robotic fish and had certain practical significance. © 2019 IEEE.

关键词： Fish

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An Efficient Network for Lane Segmentation 1

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4th International Conference on Cognitive systems and Information Processing, ICCSIP 2018

作者： Li, Haoran Zhao, Dongbin Chen, Yaran Zhang, Qichao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

As the basis of scenes understanding for autonomous driving, lane segmentation is always a challenge due to the various illumination conditions, heavy traffics and richly-textured roads. Because of the heavily biased distribution of lane/non-lane pixels, it is hard to achieve satisfying results by using image segmentation networks such as fully convolution neural networks (FCN). In this paper, we propose a new loss function to tackle the unbalanced data distribution problem. It has shown that the loss function significantly improves the performance of available segmentation networks such as FCN on the lane segmentation task. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Autonomous vehicles

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Bitcoin Fee Decisions in Transaction Confirmation Queueing Games under Limited Multi-Priority Rule

Bitcoin Fee Decisions in Transaction Confirmation Queueing G...

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2019 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2019

作者： Li, Juanjuan Yuan, Yong Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728128535

In the Bitcoin system, transaction fees serve not only as the fundamental economic incentive to stimulate miners, but also as an important tuner for the Bitcoin system to define the priorities in the transaction confirmation process. In this paper, we aim to study the priority rules for queueing transactions based on their associated fees, and in turn users' decision-making in formulating their fees in the transaction confirmation queueing game. Based on the queueing theory, we first analyzed the waiting time of users under non-preemptive limited multi-priority (LMP) rule, which is formulated to adjust users' waiting time over different priorities. We then established a game-theoretical model, and analyze users' equilibrium fee decisions. Towards the end, we conducted computational experiments to validate the theoretical analysis. Our research findings can not only help understand users' fee decisions under the LMP rule, but also offer useful managerial insights in optimizing the queueing rules of Bitcoin transactions. © 2019 IEEE.

关键词： Blockchain

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A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 961-968页

作者： Xiuqin Shang Dayong Shen Fei-Yue Wang Timo R. Nyberg the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences (CAS)Beijing 100190China the Qingdao Academy of Intelligent Industries Qingdao 266109China IEEE the Beijing Enineering Research Center of Intelligent Systems and Technology Institute of AutomationCASBeijing 100190China the Cloud Computing Center CASDongguan 523808China the School of Science at Aalto University AaltoFinland

We study the fabric spreading and cutting problem in apparel *** the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment *** reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be *** propose an iterated greedy algorithm for solving the fabric spreading and cutting *** algorithm contains a constructive procedure and an improving *** the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay *** improving loop will run until it cannot obtain any smaller lay set or the time limit is *** experiment results on 500 cases show that the proposed algorithm is effective and efficient.

关键词： Construction and improvement cutting and packing fabric spreading and cutting heuristic algorithm

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