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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是281-290 订阅

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Deep learning for massive MIMO: Challenges and Future prospects 9

Deep Learning for massive MIMO: Challenges and Future prospe...

引用

ieee 9th International conference on Communication systems and Network Technologies (CSNT)

作者： Bhatia, Vandana Tripathy, Malay Ranjan Ranjan, Priya AUUP ASET Dept Comp Sci Noida Uttar Pradesh India AUUP ASET Dept ECE Noida Uttar Pradesh India SRM Univ SEAS Dept ECE Amarawathi Andhra Pradesh India

ISBN: (纸本)9781728149769

the wireless networks today are complex, massive and have dynamic capacity demands. Increase in demand resulted into trouble in managing and monitoring the network components. thus, intelligent data-driven designs and approaches are required so that the 5th generation (5G) of mobile systems can be reformed for enabling self-organizing capabilities. thus, in the last decade, mathematical models are designed and adapted among modems. this paper presents a comprehensive outline of the emerging research on deep learning-based models for massive MIMO systems. In most of the work, Deep learning models are used for redesigning the conventional communication system. It may involve channel encoding, decoding, detection, recognition, antenna selection, modulation, etc. It will be interesting to comprehend that replacement of the communication system with a profoundly new architecture such as deep learning based autoencoder, convolutional neural network, etc. these Deep learningbased models show promising performance enhancements with a few limitations and can be efficiently used with massive MIMO.

关键词： Wireless transmission Deep learning MIMO Channel Matrix

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Accelerated Computer Vision Inference with AI on the Edge 9

Accelerated Computer Vision Inference with AI on the Edge

引用

ieee 9th International conference on Communication systems and Network Technologies (CSNT)

作者： Mittal, Varnit Bhushan, Bharat HMRITM GGSIPU CSE Dept New Delhi India

ISBN: (纸本)9781728149769

Computer vision is not just about breaking down images or videos into constituent pixels, but also about making sense of those pixels and comprehending what they represent. Researchers have developed some brilliant neural networks and algorithms for modern computer vision. Tremendous developments have been observed in deep learning as computational power is getting cheaper. But data-driven deep learning and cloud computing based systems face some serious limitations at edge devices in real-world scenarios. Since we cannot bring edge devices to the data-centers, so we bring AI to the edge devices with AI on the Edge. OpenVINO toolkit is a powerful tool that facilitates deployment of high-performance computer vision applications to the edge devices. It converts existing applications into hardware friendly and inference-optimized deployable runtime packages that operate seamlessly at the edge. the goals of this paper are to describe an in-depth survey of problems faced in existing computer vision applications and to present AI on the Edge along with OpenVINO toolkit as the solution to those problems. We redefine the workflow for deploying computer vision systems and provide an efficient approach for development and deployment of edge applications. Furthermore, we summarize the possible works and applications of AI on the Edge in future in regard to security and privacy.

关键词： Artificial Intelligence Deep learning Neural Networks Computer Vision AI on the Edge OpenVINO

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Two-stage deep learning-based wind turbine condition monitoring using SCADA data 9

Two-stage deep learning-based wind turbine condition monitor...

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9th ieee International conference on Power Electronics, Drives and Energy systems, PEDES 2020

作者： Afrasiabi, Shahabodin Afrasiabi, Mousa Parang, Benyamin Mohammadi, Mohammad Kahourzade, Solmaz Mahmoudi, Amin Shiraz University Dept. of Power and Control Engineering Shiraz Iran UniSA STEM University of South Australia Adelaide Australia College of Science and Engineering Flinders University Adelaide Australia

ISBN: (纸本)9781728156729

this paper proposes a two-stage data-driven methodology in condition monitoring (CM) of wind turbines (WTs). In the first stage, a fast and powerful network, namely a parallel generative adversarial network (PGAN) is proposed to resolve the problem of limited available information by generating artificial data. In the second stage, a robust deep network is designed based on a one-module Gabor filter oriented convolutional neural network and reformulation of a new loss function, namely robust deep Gabor network (RDGN). the experimental dataset of 3MW wind turbines in Ireland is used to verify the effectiveness of the proposed method and demonstrate the superiority of the proposed two-stage method in comparison with several state-of-the-art methods in terms of accuracy and reliability. © 2020 ieee.

关键词： Wind turbines

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An ESN based Modeling for Roll-to-Roll Printing systems

An ESN based Modeling for Roll-to-Roll Printing Systems

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data driven control and learning systems (ddcls)

作者： Zhihua Chen Tao Zhang Zheng Zhang Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Automation Huazhong University of Science and Technology Wuhan China

the following topics are dealt with: control system synthesis; adaptive control; nonlinear control systems; fault diagnosis; learning (artificial intelligence); feature extraction; observers; production engineering co... 详细信息

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

关键词： Mathematical model Printing Registers Printing machinery Training Predictive models data models

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data-driven Algorithm for Scheduling with Total Tardiness 9th

Data-driven Algorithm for Scheduling with Total Tardiness

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9th International conference on Operations Research and Enterprise systems (ICORES)

作者： Bouska, Michal Novak, Antonin Sucha, Premysl Modos, Istvan Hanzalek, Zdenek Czech Tech Univ Czech Inst Informat Robot & Cybernet Jugoslavsych Partyzanu 1580-3 Prague Czech Republic Czech Tech Univ Fac Elect Engn Dept Control Engn Karlovo Namesti 13 Prague Czech Republic

ISBN: (纸本)9789897583964

In this paper, we investigate the use of deep learning for solving a classical NP-hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. the network acts as a polynomial-time estimator of the criterion that is used in a single-pass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. the experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic.

关键词： Single Machine Scheduling Total Tardiness data-driven Method Deep Neural Networks

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Examination of image features considering short-term changes in plant water stress 9

Examination of image features considering short-term changes...

引用

9th ieee Global conference on Consumer Electronics, GCCE 2020

作者： Nakanishi, Gota Mineno, Hiroshi Shizuoka University Graduate School of Integrated Science and Technology Hamamatsu Japan

ISBN: (纸本)9781728198026

With the development of Internet of things (IoT) devices, various types of personal and industrial data can be easily collected. In agriculture, data-driven smart agriculture has become more accessible and feasible for regular farmers and for ordinary households interested in farming. For example, irrigation control considering the water stress of plants can lead to higher quality cultivation. However, it is difficult for beginner farmers to accurately determine whether a plant is under water stress or not. In this study, we propose a method that determines plant water stress using images taken with a GoPro camera in order to make it easier for farmers of all skill levels to achieve high-quality cultivation. © 2020 ieee.

关键词： Machine learning

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Function Projective Synchronization of Neutral Complex Dynamic Network with Unknown Time-varying Coupling Strength

Function Projective Synchronization of Neutral Complex Dynam...

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data driven control and learning systems (ddcls)

作者： Ying Yang Junmin Li School of Mathematics and Statistics Xidian University Xian P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper addresses the function projective synchronization control for a class of neutral complex dynamic network with nonlinear coupling strength and unknown time-varying parameters. An adaptive controller is designed for neutral complex network with time-varying coupling strength. In addition, a sufficient condition is given to guarantee the function projective synchronization of complex dynamic networks. the simulation example demonstrates the proposed method.

关键词： Synchronization Couplings Mathematical model Time-varying systems Nonlinear dynamical systems Adaptive control Security

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High-order Model-free Adaptive Iterative learning control for Velocity Tracking of Wheeled Mobile Robots

High-order Model-free Adaptive Iterative Learning Control fo...

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data driven control and learning systems (ddcls)

作者： Jiawei Li Ronghu Chi Dejing Yang School of Automation & Electronic Engineering Qingdao University of Science & Technology Qingdao Shandong JinKaiFeng Machinery Technology Co. Ltd Yishui

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, a data-driven high-order model-free adaptive iterative learning control (HMFAILC) is developed by a wheeled mobile robots (WMR) for trajectory tracking in the repetitive systems. the design and analysis of the controller only uses the I/O of the system and in the absence of any explicit model information. control performance is improved by using higher-order learning control methods to obtain more control information in the iterative process. the control performance of the control scheme is proved by mathematical analysis and simulation.

关键词： Wheels Mathematical model Mobile robots Trajectory Torque Numerical models Iterative learning control

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pth moment consensus of the disturbed multi-agent systems

pth moment consensus of the disturbed multi-agent systems

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data driven control and learning systems (ddcls)

作者： Hui Wang Junmin Li Chao He School of Mathematics and Statistics Xidian University Xian PRC

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, pth moment leader-following consensus of multi-agent systems (MASs) with additive measurement noises and time-varying communication delays is investigated. A new consensus protocol with a time-varying gain is designed. What's more, a sufficient condition of pth moment consensus of leader-following MASs is also given. Finally, an example is provided to demonstrate the effectiveness of the proposed algorithms.

关键词： Noise measurement Delays Topology Multi-agent systems Time-varying systems Consensus protocol Matrices

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A new indirect ILC method with an iterative dynamical identification of the set-point command

A new indirect ILC method with an iterative dynamical identi...

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data driven control and learning systems (ddcls)

作者： Huaying Li Ronghu Chi Qingdao University of Science & Technology Qingdao

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this work, a new indirect iterative learning control (ILC) method with an iterative dynamical identification of the set-point command is proposed. the ILC law of the set-point is derived from the dynamical linearization of a nonlinear ideal ILC law. then, the estimation law of the parameter in the ILC law is derived from the optimization of a cost function. At last, simulations demonstrate the advantages of the proposed scheme.

关键词： Mathematical model Automobiles control systems Cost function Two dimensional displays Convergence Uncertainty

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