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Boosted convolutional neural networks for motor imagery EEG decoding with multiwavelet-based time-frequency conditional granger causality analysis

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arXiv 2018年

作者： Li, Yang Lei, Mengying Zhang, Xianrui Cui, Weigang Guo, Yuzhu Huang, Ting-Wen Wei, Hua-Liang Department of Automation Sciences and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Advanced Innovation Center for Big Date-based Precision Medicine Beihang University Beijing China Department of Automation Sciences and Electrical Engineering Beihang University Beijing China Department of Mathematics Texas A&M University at Qatar Doha23874 Qatar Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3DJ United Kingdom

Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to non-stationarity of perceptual decision processes. This paper presents a novel boosted convolutional neural networks (ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the multiwavelet-based time-frequency (TF) causality analysis. Specifically, multiwavelet basis functions are first combined with Geweke spectral measure to obtain high-resolution TF-conditional Granger causality (CGC) representations, where a regularized orthogonal forward regression (ROFR) algorithm is adopted to detect a parsimonious model with good generalization performance. The causality images for network input preserving time, frequency and location information of connectivity are then designed based on the TF-CGC distributions of alpha band multichannel EEG signals. Further constructed boosted ConvNets by using spatio-temporal convolutions as well as advances in deep learning including cropping and boosting methods, to extract discriminative causality features and classify MI tasks. Our proposed approach outperforms the competition winner algorithm with 12.15% increase in average accuracy and 74.02% decrease in associated inter subject standard deviation for the same binary classification on BCI competition-IV dataset-IIa. Experiment results indicate that the boosted ConvNets with causality images works well in decoding MI-EEG signals and provides a promising framework for developing MI-BCI systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical tests

6TiSCH operation sublayer (6top) implementation on Contiki OS

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6TiSCH operation sublayer (6top) implementation on Contiki O...

International Communication Systems and Networks and Workshops, COMSNETS

作者： Lijo Thomas R Shalu J Jerry Daniel S. V. R Anand Malati Hegde Centre for Development of Advanced Computing Control and Instrumentation Group Thiruvananthapuram India Centre for Development of Advanced Computing Juhu Maharashtra IN Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9781509042517

The Internet engineering Task Force (IETF) has set up the 6TiSCH Working Group (WG) to focus on enabling IPv6 over the Time Slotted Channel Hoping (TSCH) mode of the IEEE 802.15.4-2015 standard. This paper describes our Contiki implementation of the 6TiSCH operation sublayer, 6top, which is used for dynamic scheduling of bandwidth between the neighboring sensor nodes and facilitate the On the Fly (OTF) scheduling.

关键词： Computer architecture Dynamic scheduling Bandwidth Schedules IEEE 802.15 Standard Job shop scheduling

Fast Computation of the Discrete Pascal Transform

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Fast Computation of the Discrete Pascal Transform

International Symposium on Multiple-Valued Logic

作者： Dušan B. Gajić Radomir S. Stanković Department of Computing and Control Engineering University of Novi Sad Novi Sad Serbia Department of Computer Science University of Niš Niš Serbia

The discrete Pascal transform (DPT) is a relatively recently introduced spectral transform based on the concept of the Pascal triangle which has been known for centuries. It is used in digital image processing, digital filtering, pattern recognition, watermarking, and related areas. Its applicability is limited by the O(N 2 ) asymptotical time complexity of best current algorithms for its computation, where N is the size of the function to be processed. In this paper, we propose a method for the efficient computation of the DPT in O(N log N) time, based on the factorization of its transform matrix into a product of three matrices with special structure - two diagonal matrices and a Toeplitz matrix. The Toeplitz matrix is further embedded into a circulant matrix of order 2N. The diagonalization of the circulant matrix by the Fourier matrix permits the use of the fast Fourier transform (FFT) for performing the computations, leading to an algorithm with the overall computational complexity of O(N log N). Since the entries in the Toeplitz matrix have very different magnitudes, the numerical stability of this algorithm is also discussed. We also consider the issues in implementing the proposed algorithm for highly-parallel computation on graphics processing units (GPUs). The experiments show that computing the DPT using the proposed algorithm processed on GPUs is orders of magnitude faster than the best current approach. As a result, the proposed method can significantly extend the practical applicability of the discrete Pascal transform.

关键词： Transforms Matrix decomposition Matrix converters Time complexity Numerical stability Symmetric matrices Nickel

Multi-antenna channel interpolation via Tucker decomposed extreme learning machine

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arXiv 2018年

作者： Zhang, Han Ai, Bo Xu, Wenjun Xu, Li Cui, Shuguang Department of Electrical and Computer Engineering University of California DavisCA95616 United States State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Institute of Computing Technology Chinese Academy of Science Beijing100190 China

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this letter, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine (TDELM) is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation. Copyright © 2018, The Authors. All rights reserved.

关键词： Interpolation

Mathematical modeling of the synthesis process of vinyl acetate

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Journal of Physics: Conference Series 2019年第1期1278卷

作者： D Arapov S Tikhomirov S Podvalny V Denisenko I Avcinov Chair of Higher Mathematics and Information Technology Voronezh State University of Engineering Technologies 19 Revolution Avenue Voronezh 394036 Russian Federation Chair of Information and Control Systems Voronezh State University of Engineering Technologies 19 Revolution Avenue Voronezh 394036 Russian Federation Head of the Department of Automated and Computing Systems Voronezh State Technical University 14 Moskovsky Avenue Voronezh 394026 Russian Federation

A complete mathematical modeling of the technological process of manufacturing vinyl acetate monomer by vapor-phase method has been implemented. The model is partial differential equations of the material and energy balance and catalyst aging. An algorithm for an integrated equation based on the second-order Gregg-Bulirsh-Shter rectangle symbol has been developed. The error in modeling the process is equal (CH3COOCH=CH2) ± 5.4% rel. for the target matter, and (CO2) ± 6.4% rel. for the secondary substance The kinetic coefficients of differential equations, namely the pre-exponential factors and the activation energy have been refined. The obtained data of the modeling of the vapor-phase manufacturing process of vinyl acetate are shown in the form of tables and graphic three-dimensional dependencies of quality indicators on technological parameters.

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Angular Speed control of Brushed DC Motor Using Nonlinear Method: Design and Experiment 35

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Angular Speed Control of Brushed DC Motor Using Nonlinear Me...

第35届中国控制会议

作者： ZHANG Sen GU Wanli HU Yunfeng DU Juan CHEN Hong Department of Control Science and Engineering Jilin University State Key Laboratory of Automotive Simulation and Control Jilin University Computing Center of Jilin Province

ISBN: (纸本)9781509009107

The major concentration of this study is on developing a novel control system with response and tracking accuracy features capable of precise angular speed regulation of brushed DC motor. Towards this objective, speed loop controller of brushed DC motor based on Triple-step nonlinear method has been designed. The Triple-step nonlinear method consists of the steady-state control, the reference variation-based feed-forward control, the error feedback control. The designed nonlinear controller has the advantage of concision and specific signification. The controller can greatly enhance the transient response performance of brushed DC motor. When a brushed DC motor running at low angular speed, the system appears the speed fluctuation and dead-zone characteristic. Therefore, the friction model of motor is considered in the design of controller. Current dynamic is neglected due to the fast electric response, the commonly used feedforward controller and PI feedback controller is designed. The effectiveness of the control method compared with conventional PID controllers is verified through experiments.

关键词： Brushed DC Motor Triple-step Method Friction model Angular Speed control

Random asynchronous iterations in distributed coordination algorithms

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Has “Intelligent Manufacturing” Promoted the Productivity of Manufacturing Sector?--Evidence from China’s Listed Firms

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Procedia Computer Science 2018年 139卷 299-305页

作者： Yi Qu Yong Shi Kun Guo Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA School of Computing and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Intelligent Manufacturing has attracted global and continuous attention recent years, with more and more intelligent devices and systems applied in production. In this paper, we take China’s manufacturing listed firms to investigate the productivity difference between intelligent and general manufacturing firms. By the Cobb-Douglas production function, we built a Coefficient-varying Model and used Seemingly Unrelated Regression (SUR) to estimate the time-varying trend of productivity from 2011 to 2017. The empirical results show that “Intelligent Manufacturing” has obviously promoted the Labor factor utilization efficiency and the Total Factor Productivity (TFP) through the advances in technology. But it doesn’t have universally enhancing effect of all industries. The impact of “Intelligent Manufacturing” still remains to be observed overtime.

关键词： Intelligent Manufacturing Cobb-Douglas Production Function Total Factor Productivity (TFP) Coefficient-varying Model Seemingly Unrelated Regression (SUR)

Variational autoencoders for deforming 3d mesh models

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arXiv 2017年

作者： Tan, Qingyang Gao, Lin Lai, Yu-Kun Xia, Shihong Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Computer Science and Informatics Cardiff University

3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are flexible to represent 3D animation sequences as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution. Extensive experiments demonstrate that our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space embedding and shape exploration, outperforming state-ofthe-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks