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Construction of Multilayer Porous Scaffold Based on Magnetically Guided Assembly of Microfiber

Journal of systems Science & complexity 2018年第3期31卷 581-595页

作者： LI Xingfu WANG Huaping SHI Qing SUN Tao HUANG Qiang FUKUDA Toshio Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing 100816 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper proposes a novel method of magnetically guided assembly to construct multi- layer porous scaffold for three-dimensional cell culture by apply magnetic microfibers. Microfibers are composed of biocompatible and biodegradable alginate solution with homogeneous magnetic nanopar- ticles, which are continuously spun from a microfluidic device by precise pressure control of the syringe pump. Magnetic nanoparticles enable the control of magnetic field on microfibers. Meanwhile, mag- netized device combining with a round permanent magnet are utilized to guide the distribution of spouted microfibers. The device is composed by pure iron wire arrays and wax, which stimulates pow- erful magnetic flux density and magnetic field gradients for the capture and assembly of microfibers. Thus, magnetic microfibers are spun on desired places of the magnetized device by motion control of the micromanipulation robot, and precise locations are adjusted by magnetic force couple with the assist of glass micropipette. Afterwards, microfibers are spatially organized by periodic magnetic force and crossed layer-by-layer to form micro-pore structure with both length and width of 650μm. Finally, the authors construct a multilayer microfiber-based scaffold with high porosity to provide a satisfactory environment for long-term cell culture. The experimental results demonstrate the effectiveness of the proposed method.

关键词： Magnetically guided assembly magnetic microfiber magnetized device periodic magneticforce multilayer porous scaffold.

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Multilane Detection and Tracking Based on Binocular Vision Stixel World Estimation and IPM

Multilane Detection and Tracking Based on Binocular Vision S...

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第三十八届中国控制会议

作者： Jing Li Junzheng Wang Guangtao Cui Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

Road detection is the basic component of many intelligent vehicle systems. In this paper, a robust multi-lane detection method based on binocular vision is proposed. First, a fast estimation technique of the Stixel World, which is the outside environment representation of stereo-vision, is developed. Taking advantage of the estimate for free space under the plane road hypothesis, the proposed method is robust against on-road obstacles when detecting lane markings. Then, the bird-view of the transitable area is obtained through Inverse Perspective Mapping(IPM) Transform;Steerable filter, parallel parabolas modal and RANdom SAmple Consensus(RANSAC) technique are introduced for multi-lane fitting. Experimental results in real urban driving situations indicate that our algorithm is robust under various conditions.

关键词： Multilane detection and tracking Binocular vision Stixel World RANSAC

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Learning Latent Semantic Attributes for Zero-Shot Object Detection

Learning Latent Semantic Attributes for Zero-Shot Object Det...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Kang Wang Lu Zhang Yifan Tan Jiajia Zhao Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineer Institute Beijing China

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

Zero-shot Object Detection (ZSD) aims to locate and classify instances of unseen categories. Existing methods focus on learning the mapping from visual space to semantic space, while the learning of discriminative representations for ZSD has not gained enough attention. In this paper, we demonstrate the necessity to learn discriminative semantic representations for ZSD, and propose a new end-to-end framework for this task. Our framework is able to learn discriminative semantic representations in an augmented space introduced for both user-defined and latent attributes, and refine the user-defined attributes with the help of unseen and external classes. The proposed method is extensively evaluated on two challenging ZSD datasets, and the experimental results show that our method significantly outperforms several existing methods.

关键词： Visualization Conferences Semantics Object detection Tools Boosting Task analysis

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Parallel Structure of Six Wheel-legged Robot Model Predictive Tracking control based on Dynamic Model

Parallel Structure of Six Wheel-legged Robot Model Predictiv...

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Chinese Automation Congress (CAC)

作者： Bomeng Wang Junzheng Wang Shoukun Wang Jiehao Li Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

In the research of wheel-legged robots, trajectory tracking control is the inevitable requirement. In this paper, a trajectory tracking control scheme based on model predictive control for the parallel mechanism of six wheel-legged robot is proposed. A dynamic model was employed to reduce the amount of calculations for planning and control. In the model, the dynamic nonlinear constraints such as the tire slip rate and the roll caused by the lateral acceleration need to be considered. Finally, the control method is verified in the dynamic model. The simulation results show that all dynamic constraints can be maintained within a given interval and the control method has obvious tracking effect improvement.

关键词： Manganese

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Probabilistic Plan Recognition Under Temporal Logic Tasks

Probabilistic Plan Recognition Under Temporal Logic Tasks

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第三十八届中国控制会议

作者： Wentao Yu Hao Fang Daiying Tian School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

In this paper, we consider the plan recognition problem in the real-time strategy game. A probabilistic plan recognition algorithm is proposed to predict the future goals and identify the temporal logic tasks of the non-cooperative agent based on the observations. In order to model the temporal logic tasks, the plan library is composed of the Finite Transition system and Nondeterministic B ¨uchi Automation. Specially, we provide a unified framework to combine the plan recognition and the planning, and propose the probability calculation algorithm to calculate the posterior probability distribution of the goals and tasks. Finally, we verify the effectiveness of the proposed algorithm by the compared simulations.

关键词： Probabilistic Plan Recognition Linear Temporal Logic Büchi Automation Goal Prediction

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Impedance Modeling of PMSG Wind Farms from the Machine Side DC-Port with Outer Power Loop

Impedance Modeling of PMSG Wind Farms from the Machine Side ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jiajun Wang Xiaozhong Liao Bin Liu Xi Chen Xiangdong Liu Xiaoqin Lian Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing 100081 China GEIRI North America CA 95134 USA Beijing Technology and Business University Beijing 100048 China

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Due to the advantages of large unit capacity, high efficiency and high reliability, direct-drive permanent magnet synchronous generators (PMSGs) wind turbines (WTs) have been widely used in the offshore wind power generation. The power loop controller aims to maintain the DC-Bus voltage of each PMSG-WT is equal to the others, guaranteeing that multiple WTs can be connected in DC serial parallel collection. In this paper, the DC-Port sequence impedance model of the PMSG-WT is established based on the harmonic linearization method, which takes the outer power loop into consideration. The impedance model is further verified by point-by-point scanning in MATLAB/Simulink. The proposed DC-Port impedance model of the PMSG-WT facilitates the establishment of offshore direct-drive wind farms, and is of great significance for analyzing and improving the system stability.

关键词： Impedance Mathematical model Harmonic analysis Wind farms Generators Power system stability Topology

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Learning Hidden Markov Models for Linear Gaussian systems with Applications to Event-based State Estimation

arXiv

引用

arXiv 2020年

作者： Zheng, Kaikai Shi, Dawei Shi, Ling State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong

This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimation problems. An indirect modeling approach is developed, wherein a state space model (SSM) is firstly identified for a Gaussian system and the SSM is then used as an emulator for learning an HMM. In the proposed method, the training data for the HMM are obtained from the data generated by the SSM through building a quantization mapping. Parameter learning algorithms are designed to learn the parameters of the HMM, through exploiting the periodical structural characteristics of the HMM. The convergence and asymptotic properties of the proposed algorithms are analyzed. The HMM learned using the proposed algorithms is applied to event-triggered state estimation, and numerical results on model learning and state estimation demonstrate the validity of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

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Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization

arXiv

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arXiv 2021年

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Xie, Lihua Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China School of Electronic and Information Engineering Tongji University Shanghai200082 China Ministry of Education Beijing100081 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

The non-smooth finite-sum minimization is a fundamental problem in machine learning. This paper develops a distributed stochastic proximal-gradient algorithm with random reshuffling to solve the finite-sum minimization over time-varying multi-agent networks. The objective function is a sum of differentiable convex functions and non-smooth regularization. Each agent in the network updates local variables by local information exchange and cooperates to seek an optimal solution. We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution with an (formula presented) convergence rate, where T is the total number of iterations. Finally, some comparative simulations are provided to verify the convergence performance of the proposed algorithm. © 2021, CC BY.

关键词： Optimal systems

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Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power systems

Data Augment Using Deep Convolutional Generative Adversarial...

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Chinese control Conference (CCC)

作者： Jiamin Li Hongying Yang Liping Yan Zonghan Li Daowei Liu Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P. R. China China Electric Power Research Institute Beijing P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Real-time and accurate transient stability assessment (TSA) is essential for planning, operation and control of power systems. As a data-driven technology, deep learning method plays an important role in TSA. Nevertheless, the fact that instability situations rarely occur would lead to a challenging class-imbalanced issue, which brings great difficulties to the deep learning methods. Besides, feature extraction from high dimensional input data and transient stability classification seem extremely difficult for conventional classification methods. To address these problems, this paper develops a class-imbalanced TSA method by combining nonlinear data synthesis method with the deep learning classification model. Firstly, deep convolutional generative adversarial network (DCGAN) is conducted to generate unstable instances based on the existing samples to balance the proportion of different classes. Furthermore, the convolutional neural network (CNN) is utilized to extract the nonlinear mapping relationship between the disturbance features and the stability category and realize TSA. Finally, the IEEE 10-machine, 39-bus New England system is utilized to verify the validity and effectiveness of the proposed method.

关键词： Power system stability Feature extraction Generative adversarial networks Transient analysis Machine learning Training

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Shaping effect of phase-locked loop on impedance characteristics of PMSG wind turbines connected to weak-grid

Shaping effect of phase-locked loop on impedance characteris...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Wan, Feiya Li, Zhen Liu, Bin Li, Guanghui Dong, Xiaoliang Chen, Xi Yu, Samson S. Oo, Amanullah M. T. Shan, Zhenyu Liu, Xiangdong Beijing Institute of Technology School of Automation and Key Laboratory for Intelligent Control Decision on Complex Systems Beijing China China Electric Power Research Institute Beijing China State Grid Corporation of China and North China Branch of State Grid Corporation of China Beijing China GEIRI North America United States Deakin University School of Engineering 75 Pigdon Rd Waurn Ponds Victoria Australia North China University of Technology School of Electrical and Control Engineering Beijing China

ISBN: (纸本)9781728155081

With the booming installed capacity of permanent magnet synchronous generator (PMSG) for wind energy generation, the grid becomes weaker and the oscillation events are frequently observed in weak-grid-tied large-scale grid-connected PMSG wind farms. Although the impedance-based method is effective to analyze the stability of grid-connected systems, the mathematical relationship between external impedance characteristics and the internal controllers has not been quantitatively analyzed in depth, which is beneficial to the controller design. This paper studies the influence of phase-locked loop (PLL) on the impedance characteristics based on the analytical sequence impedance model of PMSG with typical topology and control structure. The shaping effect of PLL on the impedance characteristics is described so as to clearly describe the relationship between the external impedance characteristics of the wind turbines and the internal controller. Finally, the stability analysis of the grid-connected PMSG wind turbine system is provided in Matlab and simulation results are given to verify the oscillating mechanism caused by PLL. © 2020 IEEE.

关键词： Phase locked loops

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