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IEEE International Conference on Communications

作者： Meng Zhang Miaowen Wen Xiang Cheng Liu-Qing Yang School of Electronics Engineering and Computer Science Peking University School of Electronic and Information Engineering South China University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467364300

Differential spatial modulation (DSM) is a newly proposed differential modulation technique tailored to spatial modulation (SM), which requires no channel state information (CSI) at the receiver. DSM can offer flexible tradeoff between the reception reliability and the system complexity. In this paper, we are the first to study the adoption of DSM in a dual-hop amplify-and-forward (AF) relaying system, which consists of a two-antenna source, a single-antenna relay, and a single-antenna destination, so as to reduce the burden of channel tracking on both the relay and the destination. We derive a general upper bound on the average bit error probability (ABEP) achieved by the system. Moreover, an exact closed-form ABEP expression and the asymptotic result are provided for BPSK signaling in Rayleigh fading environment. The same system setup with the adoption of SM at the source is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a 3dB signal-to-noise power ratio (SNR) penalty of the considered system compared with the benchmark.

关键词： Relays Upper bound Binary phase shift keying Signal to noise ratio Transmitting antennas Wireless communication

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Index Modulated OFDM with ICI Self-Cancellation for V2X Communications

Index Modulated OFDM with ICI Self-Cancellation for V2X Comm...

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International Conference on Computing, Networking and Communications

作者： Yuke Li Miaowen Wen Xiang Cheng Liu-Qing Yang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronic and Information Engineering South China University of Technology School of Electronics Engineering and Computer Science Peking University

ISBN: (纸本)9781467385800

Recent years have witnessed the rapid development of intelligent transportation systems (ITS), which urgently call for highly reliable and effective communication technologies for vehicle-to-vehicle and vehicle-to-infrastructure (V2X) applications. Orthogonal frequency division multiplexing (OFDM) is widely believed to be the most promising candidate among these applications. However, the orthogonality of subcarriers in OFDM can be easily destroyed by the Doppler-induced inter-carrier interference (ICI), which is very common in V2X channels. In this paper, we propose a novel scheme which integrates the ICI self-cancellation technique into the index modulated (IM-) OFDM framework. Via careful designs, the proposed scheme can not only inherit the advantages of IM-OFDM but also suppress the ICI from active subcarriers. Simulations validate that in V2X channels, the proposed scheme significantly outperforms existing IM-OFDM and more importantly shows better performance than the conventional OFDM with ICI self-cancellation without sacrificing the spectral efficiency.

关键词： OFDM Vehicles Indexes Detectors Modulation Computational complexity Doppler shift

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Modified Iterative-Learning-control-Based Ramp Metering Strategies for Freeway Traffic control With Iteration-Dependent Factors

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IEEE Transactions on Intelligent Transportation Systems 2012年第2期13卷 606-618页

作者： Zhongsheng Hou Jingwen Yan Jian-Xin Xu Zhenjiang Li Advanced Control Systems Laboratory of School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing China

For a freeway traffic system with strict repeatable pattern, iterative learning control (ILC) has been successfully applied to local ramp metering for a macroscopic freeway environment by formulating the original ramp metering problem as an output tracking, disturbance rejection, and error compensation problem. In this paper, we address the freeway traffic ramp-metering system under a nonstrict repeatable pattern. ILC-based ramp metering and ILC add-on to ALINEA strategies are modified to deal with the presence of iteration-dependent parameters, iteration-dependent desired trajectory, and input constraints. Theoretical analysis and extensive simulations are used to verify the effectiveness of the proposed approaches.

关键词： Traffic control Trajectory Mathematical model Vehicles Equations Feedback control

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Multi-room occupancy estimation through adaptive gray-box models

Multi-room occupancy estimation through adaptive gray-box mo...

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IEEE Annual Conference on Decision and control

作者： A. Ebadat G. Bottegal M. Molinari D. Varagnolo B. Wahlberg H. Hjalmarsson K. H. Johansson ACCESS and the Department of Automatic Control School of Electrical engineering KTH Royal Institute of Technology Sweden Division of Signals and Systems Department of Computer Science Electrical and Space Engineering Lulea University of Innovation and Technology Lulea Sweden

ISBN: (纸本)9781479978878

We consider the problem of estimating the occupancy level in buildings using indirect information such as CO_2 concentrations and ventilation levels. We assume that one of the rooms is temporarily equipped with a device measuring the occupancy. Using the collected data, we identify a gray-box model whose parameters carry information about the structural characteristics of the room. Exploiting the knowledge of the same type of structural characteristics of the other rooms in the building, we adjust the gray-box model to capture the CO_2 dynamics of the other rooms. Then the occupancy estimators are designed using a regularized deconvolution approach which aims at estimating the occupancy pattern that best explains the observed CO_2 dynamics. We evaluate the proposed scheme through extensive simulation using a commercial software tool, IDA-ICE, for dynamic building simulation.

关键词： Vehicle Occupancy Rooms Structural properties Buildings building simulation BOX MODELS Carbon Dioxide

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Time series analysis based on the biologically inspired modular approach

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Procedia computer science 2017年 120卷 848-853页

作者： Sergey Yarushev Alexey Averkin Dubna International University of Nature Society and Man Institute of system analysis and management Universitetskaya str. 19 Dubna 141980 Russian Federation FederalResearch Centre of Computer Science and Control of Russian Academy of Sciences Vavilova str. 40 Moscow 119333 Russian Federation

This paper presents the biologically inspired approach to time series forecasting area. We consider the most important characteristics of modular architectures of neural networks and their advantages under traditional monolithic neural networks. The main idea of this paper is take answer - why modular neural networks have so high performance in many tasks. In addition, we present few examples of modular approaches, which can be applicable for time series forecasting problem. Also we presenting forecasting system which now developing.

关键词： time series forecasting bica biologically inspired systems modular neural networks anfis soft computing

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On the control discrete linear repetitive processes with smoothing effects *

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IFAC Proceedings Volumes 2009年第13期42卷 342-347页

作者： B. Cichy K. Gałkowski E. Rogers Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK.

Abstract Repetitive processes are a distinct class of two-dimensional (2D) systems (i.e. information propagation in two independent directions occurs) of both systems theoretic and applications interest. In particular, these processes are characterized by a series of sweeps through dynamics defined over a finite duration where the output produced on the previous pass critically contributes to the current one and so on. Also the process is reset before the start of each new pass and in the majority of the currently known results it is assumed that the previous pass profile is unaffected by the resetting and other not directly modeled effects. In practice this may not be the case and in such cases there is a need to adequately represent this feature in the models used for analysis. In this paper control law design algorithms for one representation of the case when the previous pass profile is significantly modified before the next pass begins are developed.

关键词： Stability analysis control law design LMI

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Closed-Loop control of a Novel 2-DOF MEMS Nanopositioner with Electrothermal Actuation *

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IFAC Proceedings Volumes 2013年第5期46卷 391-398页

作者： Anthony G. Fowler Micky Rakotondrabe S.O. Reza Moheimani School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia FEMTO-ST Institute UMR CNRS 6174 - UFC / ENS MM / UTBM Automatic Control and Micro-Mechatronic Systems depart. AS2M 25000 Besançon France

The design, characterization and control of a novel 2-DOF MEMS nanopositioner is presented, with Z-shaped electrothermal actuators being used to position the device's central stage. Whereas the more commonly-used V-shaped electrothermal actuator only allows displacements in one direction, the design of the Z-shaped beams used in the presented device allows two actuators to be coupled back-to-back to achieve bidirectional motion along each of the two axes. Testing of the device shows that stage displacements in excess of ϕ5 μm are achievable for both the x and y axes. The device features integrated displacement sensors based on polysilicon electrothermal heaters, which are supplied with an electrical bias voltage that results in Joule heating. The resistance of each heater varies depending on the position of the central stage, with two heaters being used per axis in a differential configuration. The displacement measurements are utilized as part of an implemented closed-loop control scheme that uses both feedforward and feedback mechanisms based on the principle of internal model control. Experimental testing shows that the use of the controller enhances the static and dynamic performance of the system, with particular improvements being seen in the device's reference tracking, response time and cross-coupling rejection.

关键词： MEMS nanopositioning electrothermal actuator electrothermal sensor feedforward feedback

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Learning Communities from Equilibria of Nonlinear Opinion Dynamics

Learning Communities from Equilibria of Nonlinear Opinion Dy...

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IEEE Conference on Decision and control

作者： Yu Xing Anastasia Bizyaeva Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and with Digital Futures Stockholm Sweden Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the intercommunity connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms.

关键词： Accuracy Heuristic algorithms Image edge detection Stochastic processes Clustering algorithms Mathematical models Numerical models Detection algorithms

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