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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhao, Jianming Zeng, Peng Shang, Wenli Tong, Guoyu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811581007

Software Defined Networking (SDN) is a new type of network architecture, which provides an important way to implement automated network deployment and flexible management. However, security problems in SDN are also inevitable. DDoS attack belongs to one of the most serious attack types, which is fairly common for today’s Internet. In SDN security fields, DDoS attack detection research has been received more and more attention. In this paper, a DDoS attack detection method based on one-class SVM in SDN is proposed, which provides a better detection accuracy. Furthermore, two new feature vectors, including middle value of flow table item’s duration and protocol data traffic percentage, are extracted to integrate into the item of 11 feature vectors. Additionally, basing on selection and construction method of the 11 feature vectors, a DDoS attack behavior model is established by using one-class SVM algorithm, and the self-adaptation genetic algorithm is designed to optimize the corresponding parameters of the Gaussian kernel of one-class SVM. The experimental results in SDN show that, the proposed new feature vectors are shown to more better detection accuracy, and the proposed method is more feasible by comparing with the BP neural network and RBF neural network algorithms under the same 11 features vectors. © Springer Nature Singapore Pte Ltd 2020.

关键词： Genetic algorithms

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Real-Time Energy Management of Microgrid system Based on Policy-Based Reinforcement Learning

SSRN

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SSRN 2022年

作者： Liu, Ding Zang, Chuanzhi Zeng, Peng Li, Wanting Wang, Xin Liu, Yuqi Xu, Shuqing Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institute of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang University of Technology Shenyang110870 China

Electric power grids are changing from traditional power systems to modern smart integrated power systems. The microgrid (MG) plays a vital role in this process through the integration of distributed renewable energy resources (RESs) and energy storage systems (ESSs). However, wind and solar radiation are characterized by intermittence, randomness and fluctuation. These uncertainties cannot be ignored in microgrids with high penetration of RES and random load demand, which challenges the effective and economic management of MGs. To achieve real-time economically optimal scheduling of microgrids, we proposed a real-time energy management strategy. Unlike traditional model-based approaches, this strategy is learning-based and does not need the knowledge of system uncertainties. The energy management problem of a microgrid is formulated as a Markov decision process (MDP) solved using policybased deep reinforcement learning (DRL). In the proposed RL-based approach, two different neural networks are used. One takes the real-time system state of the microgrid as inputs and makes the decision to obtain the optimal control policy © 2022, The Authors. All rights reserved.

关键词： Deep learning

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An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

arXiv

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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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A visualization recommendation approach based on machine learning 10

A visualization recommendation approach based on machine lea...

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2020 10th International Workshop on Computer Science and Engineering, WCSE 2020

作者： Wei, Shichao Li, Xin Song, Peiyin Zhou, Xiaofeng Zhang, Yichi Li, Shuai Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Network Control System Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Shenyang University of Technology China

ISBN: (纸本)9789811447877

As an important means of data analysis, data visualization is used by more and more people. For most people who don't have visualization technology expertise, data visualization has some Visualization recommendation aims to lower the barrier to exploring basic visualizations by automatically generating results for analysts to search and *** paper proposes a visual recommendation method based on machine learning, which can learn the most meaningful visualization results from many visualization practice datasets and mark ***, 22 data features and corresponding meaningful visualization types are extracted from 30 real visualization datasets. Then, binary classifiers are used to train the classification model, from which we can learn meaningful visualization and use crowdsourced testsets to test the ***, the results of multiple classifiers are fused to vote for multiple meaningful charts in the *** show that this method can effectively learn the meaningful visualization types in datasets, mark and recommend them to users. © WCSE 2020.

关键词： Visualization

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An MR Safe Rotary Encoder Based on Eccentric Sheave and FBG Sensors

An MR Safe Rotary Encoder Based on Eccentric Sheave and FBG ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shaoping Huang Anzhu Gao Zicong Wu Chuqian Lou Yanjun Wang Guang-Zhong Yang The Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University 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 The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology

MRI-guided robotic systems are emerging platforms for minimally invasive intervention because of high positioning accuracy and excellent tissue contrast. MR safe encoders are critical components for closed-loop robotic control. This paper develops an MR safe absolute rotary encoder based on eccentric sheave and FBG sensors. The eccentric sheave transforms the rotational motion of the shaft to the bending deflection of the beam on which FBG sensors are integrated. A model is built by establishing the relationship of the kinematics of the sheave, the mechanical properties of the beam with unknown length, and the strain model of two Fiber Bragg Grating (FBG) sensors. A Pseudo-Rigid Body (PRB) 3R model is used to solve a set of constrained equations for accurate rotary encoding. A prototype is built to calibrate the parameters and validate the accuracy of the encoder and its MR compatibility. Results show that the maximum angular error is 1.6°, and the RMS error is 0.46°. MRI shows that no noticeable artifacts are observed, and the Signal to Noise Ratio (SNR) is not affected. The results demonstrate the potential of the proposed method for it to be integrated with MR safe robots with easy fabrication, compact structures, and continuous measurement.

关键词： Mechanical sensors Shafts Analytical models Kinematics Transforms Fiber gratings Robot sensing systems

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Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain Stimulation Electrode Implantation

Pneumatically Actuated MR-Safe Parallel Robot for Deep Brain...

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IEEE International Conference on robotics and Biomimetics

作者： Weihang Huang Shenghao Dai Anze Xu Xianyi He Shaoping Huang Anzhu Gao School of Mechanical Engineering Shanghai Jiao Tong University Shanghai Jiao Tong University Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665405362

Deep brain stimulation (DBS) has been widely used in clinical practice as an effective therapy for neurological disorders. MR-conditional robot-assisted electrode implantation has attracted more attention for DBS because of its great accuracy and high-quality guiding images by using MRI. This paper develops a pneumatically actuated MR-safe parallel robot for deep brain electrode implantation. Its fully plastic components and MR-safe pneumatic actuators enable the robotic system to be MR-safe, posing no known hazards in MR environment. A 6-DOF parallel robot is developed using six slider-crank mechanisms for prismatic motions. Inverse kinematics is built to investigate its workspace and achieve the precise control. Experiments are carried out to validate the feasibility, and results shows that the average error of the motion is 0.11 mm. The developed parallel robot shows the potential advantages in the automatic and precise robot-assisted neurosurgical surgery in future.

关键词： Electrodes Pneumatic actuators Neurological diseases Parallel robots Magnetic resonance imaging Satellite broadcasting Deep brain stimulation

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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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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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