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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image processing

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NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui Hu Jiaming Cai Wangjie Zheng Yang Yang Hong-Bin Shen Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools.

关键词： Three-dimensional displays Feature extraction Image reconstruction Task analysis Solid modeling Electron microscopy Convolutional neural networks

Wireless communication using chaotic baseband waveform

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arXiv 2019年

作者： Yao, Jun-Liang Sun, Yu-Zhe Ren, Hai-Peng Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China Institute for Complex System and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

Some new properties of the chaotic signal have been implemented in communication system applications recently. They include: (i) chaos is proven to be the optimal communication waveform in the sense of very simple matched filter being used to achieve maximum signal to noise ratio;(ii) the amount of information contained in a chaotic signal is unaltered by a wireless multipath channel;and (iii) chaos property can be used to resist multipath effect. All these support the application of chaos in a practical communication system. However, due to the broadband property of the chaotic signal, it is very difficult for a practical transducer or antenna to convert such a broadband signal into a signal that would be suitable for practical band-limited wireless channel. Thus, the use of chaos property to improve the performance of conventional communication system without changing the system configuration becomes a critical issue in communication with chaos. In this paper, chaotic baseband waveform generated by a chaotic shaping filter is used to show that this difficulty can be overcome. The generated continuous-time chaotic waveform is proven to be topologically conjugate to a symbolic sequence, allowing the encoding of arbitrary information sequence into the chaotic waveform. A finite impulse response filter is used to replace the impulse control in order to encode information into the chaotic signal, simplifying the algorithm for high speed communication. A wireless communication system is being proposed using the chaotic signal as the baseband waveform, which is compatible with the general wireless communication platform. The matched filter and decoding method, using chaos properties, enhance the communication system performance. The Bit Error Rate (BER) and computational complexity performances of the proposed wireless communication system are analyzed and compared with the conventional wireless systems. The results show that the proposed chaotic baseband waveform of our

关键词： Bit error rate

Synchronization stability and circuit experiment of hyperchaos with time delay using impulse control

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arXiv 2019年

作者： Ren, Hai-Peng Tian, Kun Bai, Chao Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian710048 China School of Mechatronics Engineering Xi’an Technological University Xian710048 China

Secure communication using hyperchaos has a better potential performance, but hyperchaotic impulse circuits synchronization is a challenging task. In this paper, an impulse control method is proposed for the synchronization of two hyperchaotic Chen circuits. The sufficient conditions for the synchronization of hyperchaotic systems using the impulse control are given. The upper bound of the impulse interval is derived to assure the synchronization error system to be asymptotically stable. Simulation and circuit experiment show the correctness of the analysis and feasibility of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Synchronization

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

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

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

Distributed Nonconvex Optimization: Gradient-free Iterations and ϵ-Globally Optimal Solution

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arXiv 2020年

作者： He, Zhiyu He, Jianping Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Automatic Control Laboratory ETH Zurich Zurich8092 Switzerland

Distributed optimization utilizes local computation and communication to realize a global aim of optimizing the sum of local objective functions. This article addresses a class of constrained distributed nonconvex optimization problems involving univariate objectives, aiming to achieve global optimization without requiring local evaluations of gradients at every iteration. We propose a novel algorithm named CPCA, exploiting the notion of combining Chebyshev polynomial approximation, average consensus, and polynomial optimization. The proposed algorithm is i) able to obtain ϵ-globally optimal solutions for any arbitrarily small given accuracy ϵ, ii) efficient in both zeroth-order queries (i.e., evaluations of function values) and inter-agent communication, and iii) distributed terminable when the specified precision requirement is met. The key insight is to use polynomial approximations to substitute for general local objectives, distribute these approximations via average consensus, and solve an easier approximate version of the original problem. Due to the nice analytic properties of polynomials, this approximation not only facilitates efficient global optimization, but also allows the design of gradient-free iterations to reduce cumulative costs of queries and achieve geometric convergence for solving nonconvex problems. We provide a comprehensive analysis of the accuracy and complexities of the proposed algorithm. © 2020, CC BY-NC-SA.

关键词： Polynomial approximation

A distributed alternating direction method of multipliers algorithm for consensus optimization

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A distributed alternating direction method of multipliers al...

Chinese Automation Congress (CAC)

作者： Xia Zhang Ding Liu Fei Yu Duqiao Zhao National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Xian China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian China

The Alternating Directions Methods of Multipliers (ADMM) are widely used in many fields of scientific computing in recent years. This method applies iterative computation to the information exchange between individual agent and neighbor. However, despite the success of traditional centralized ADMM in some application environments, its applicability is limited in global convergence center by its communication requirements. In our paper, we provide the linear convergence rate for this distributed consensus optimization problem, which satisfies strongly convex local objective functions. Then, the properties of the local objective function and the parameters of the algorithm, the theoretical convergence rate is given according to the network topology.

关键词： Convergence Optimization Linear programming Convex functions Numerical models Signal processing algorithms information processing

Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

Development of Sheet Palletizing Manipulator Based on Multi-link Mechanism

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Research Square

Research Square 2020年

作者： Chen, Zhihua Wang, Shoukun Liu, Quan Wang, Junzheng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing100081 China New Energy Laboratory College of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China

In view of the disadvantages of Traditional tandem joint type thin-plate palletizing robots using push-down adsorption sorting, such as moving path complexity, large occupied space, joint error stack and low end control precision, a novel multi-link mechanism thin-plate palletizing robot is developed. The kinematics analysis is carried out based on the analysis of the overall structure for the manipulator, a genetic algorithm based on the stroke speed ratio K, the transmission angle γ and the four-bar size is proposed to realize the optimal design of the four-link rocker arm mechanism. Secondly, a finite element based overall structure analysis method is proposed, which obtains the most affected part of the vibration process of a manipulator and performs load verification to prevent early warning and reference of faults caused by mechanical resonance. To solve the control problem of nonlinear strongly coupled dynamic systems, an iterative learning fuzzy adaptive control algorithm is proposed to achieve the smooth running of the manipulator and the high-precision tracking of the end centroid trajectory. The experimental results verify the feasibility and effectiveness of the proposed multi-link controllable thin-plate palletizing manipulator's overall structural design and iterative learning control method, which is well applied in practical engineering. © 2020, CC BY.

关键词： Manipulators

Water Supply Prediction Based on Initialized Attention Residual Network

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Water Supply Prediction Based on Initialized Attention Resid...

第三十九届中国控制会议

作者： Yuhao Long Yeming Zhang Jingcheng Wang Miaoshun Bai Dept of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of China Shanghai Municipal Engineering Design Institute(Group)Co. Ltd Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University

Real-time and accurate water supply forecast is crucial for water supply control. However, most existing methods are likely affected by factors such as weather and holidays, which lead to a decline in the reliability of water supply *** this paper, we address a generic artificial neural network, Initialized Attention Residual Network(IARN), to tackle the time series prediction problem in water supply domain. Specifically, instead of continuing to use the recurrent neural network(RNN)in time-series tasks, we try to build a convolution neural network(CNN) to recede the disturb from other factors and get a more credible results. Experiments show that our method achieves state-of-the-art performance on several datasets in terms of accuracy,robustness and generalization ability. The source code of our method will available at https://***/SJTU-L-dd/IARN.

关键词： Neural networks Prediction methods Water supply Time-series analysis Attention module Residual network