检索结果-内蒙古大学图书馆

9th International Conference on Learning Representations, ICLR 2021

作者： Silva, Arlei Kocayusufoglu, Furkan Jafarpour, Saber Bullo, Francesco Swami, Ananthram Singh, Ambuj Computer Science Department UC Santa BarbaraCA93106-5110 United States Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States U.S. Army Research Lab AdelphiMD20783 United States

The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying physics (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing edge flows and solves the problem using bilevel optimization and deep learning. More specifically, we learn regularizers that depend on edge features (e.g., number of lanes in a road, resistance of a power line) using neural networks. Empirical results show that our method accurately predicts missing flows, outperforming the best baseline, and is able to capture relevant physical properties in traffic and power networks. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Forecasting Slow-Wave Sleep Deficiency Through Stress-Related Markers in Forehead EEG

Forecasting Slow-Wave Sleep Deficiency Through Stress-Relate...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Cheng-Hua Su Li-Wei Ko Tzyy-Ping Jung Julie Onton Shey-Cherng Tzou Jia-chi Juang Chung-Yao Hsu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Science and Environmental Biology Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA USA College of Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Institute for Neural Computation University of California San Diego La Jolla CA USA Department of Neurology Sleep Disorders Center Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Neurology College of Medicine Kaohsiung Medical University Kaohsiung Taiwan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Sleep quality is critical for human well-being. Lack of sleep and poor sleep quality impair daily cognitive functions and health. While stress has been recognized as a detrimental factor on sleep quality, the relationship between pre-sleep stress level, resting EEG and subsequent sleep structure remains to be explored. This study presents a novel approach that evaluates pre-sleep stress levels using a 2-channel EEG to predict slow-wave sleep (SWS) deficiency. We recorded forehead EEG immediately before sleep onset, then utilized power spectra and entropy analysis to extract stress-related neurological features, including beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn), and spectral entropy (SpEn). We found that individuals with SWS deficiency exhibited signs of stress, such as a robust beta/delta correlation, higher alpha asymmetry, and increased FuzzEn. Conversely, individuals with ample SWS displayed weak beta/delta correlation and reduced FuzzEn in EEG recordings. Finally, we tested the robustness of the selected neuro markers with two supervised learning models and found that the selected markers predict SWS deficiency with an accuracy above 70%. Our study demonstrated that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. The proposed method can be integrated with a portable EEG device and sleep-improving interventions to develop a personalized sleep-improvement solution.

关键词： Correlation Forehead Sleep Supervised learning Feature extraction Brain modeling Electroencephalography Entropy Forecasting Stress

来源：评论

学校读者我要写书评

暂无评论

Embedding Adaptive Features in the ArduPilot control Architecture for Unmanned Aerial Vehicles

Embedding Adaptive Features in the ArduPilot Control Archite...

引用

IEEE Conference on Decision and control

作者： Peng Li Di Liu Xin Xia Simone Baldi School of Cyber Science and Engineering Southeast University Nanjing China School of Computation Information and Technology Technical University of Munich Germany School of Cyber Science and Engineering Southeast University China School of Mathematics Southeast University Nanjing China Delft Center for Systems and Control TU Delft the Netherlands

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The operation of Unmanned Aerial Vehicles (UAVs) is often subject to state-dependent alterations and unstructured uncertainty factors, such as unmodelled dynamics, environmental weather disturbances, aerodynamics gradients, or changes in inertia and mass due to payloads. While a large number of autopilot solutions have been proposed to operate UAVs, none of these solutions is able to counteract the effects of state-dependent and unstructured uncertainties online by parameter estimation and adaptive control techniques. This work presents a systematic integration of adaptive control into ArduPilot, a popular open-source autopilot suite maintained by a large community of UAV developers. Adaptation features are embedded in the ArduPilot control structure without altering the original architecture, to allow users to use the autopilot suite as usual. Tests show that the proposed adaptive ArduPilot provides consistent improved performance in several uncertain flight conditions. The source code of the proposed adaptive ArduPilot is released at https://***/Friend-Peng/Adaptive-ArduPilot-Autopilot.

关键词： Uncertainty Systematics Parameter estimation Source coding Autonomous aerial vehicles Aerodynamics Autopilot

来源：评论

学校读者我要写书评

暂无评论

Data-driven Self-triggered control via Trajectory Prediction

arXiv

引用

arXiv 2022年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a data-driven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the pre-collected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples. © 2022, CC BY.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

Convergence, Consensus and Dissensus in the Weighted-Median Opinion Dynamics

arXiv

引用

arXiv 2022年

作者： Mei, Wenjun Hendrickx, Julien M. Chen, Ge Bullo, Francesco Dörfler, Florian The Department of Mechanics and Engineering Science Peking University China The Institute of Information and Communication Technologies Electronics and Applied Mathematics UCLouvain Belgium The Academy of Mathematics and Systems Science Chinese Academy of Sciences China The Center of Control Dynamical-Systems and Computation University of California Santa Barbara United States Automatic Control Laboratory ETH Zurich Switzerland

Mechanistic and tractable mathematical models play a key role in understanding how social influence shapes public opinions. Recently, a weighted-median mechanism has been proposed as a new micro-foundation of opinion dynamics and validated via experimental data. Numerical studies also indicate that this new mechanism recreates some non-trivial real-world features of opinion evolution. In this paper, we conduct a thorough theoretical analysis of the weighted-median opinion dynamics. We fully characterize the set of all equilibria, and we establish the almost-sure finite-time convergence for any initial condition. Moreover, we prove a necessary and sufficient graph-theoretic condition for the almost-sure convergence to consensus, as well as a sufficient graph-theoretic condition for almost-sure persistent dissensus. It turns out that the weighted-median opinion dynamics, despite its simplicity in form, exhibit rich dynamical behavior that depends on some delicate network structures. To complement our sufficient conditions for almost-sure dissensus, we further prove that, given the influence network, determining whether the system almost surely achieves persistent dissensus is NP-hard, which reflects the complexity the network topology contributes to opinion *** Codes 93E03, 93A30, 91D30 Copyright © 2022, The Authors. All rights reserved.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

Optimization Strategy of Bio-Inspired Metaheuristic Algorithms Tuned PID controller for PMBDC Actuated Robotic Manipulator

引用

Procedia Computer Science 2020年 171卷 2040-2049页

作者： Sudarshan K. Valluru Madhusudan Singh Center for Control of Dynamical Systems and Computation Department of Electrical Engineering Delhi Technological University Delhi-110042 India

The present article makes an attempt to evaluate the computational performance of genetic meta heuristic optimized control algorithms. Here, the multi-objective bio inspired algorithm (MOGA) and adaptive particle swarm optimization (APSO) algorithm are used to tune linear PID (L-PID) and nonlinear PID(NL-PID) controllers to implement performance and execution control of permanent magnet brushed DC (PMBDC) motor actuated robotic manipulator. The MOGA, APSO optimised nonlinear and linear PID controllers have been validated for their response efficacy and compared in respect of the steady-state error, overshoot and settling time of PMBDC driven robotic Manipulator. The efficacy of metaheuristics such as APSO and MOGA tuned NL-PID controller are better as compared to the L- PID controlled objects. Experimental results show that the NL-PID controller mends the controlled objects’ performace with a reduction in both the overshoot as well as the settling time, if tuned either with MOGA or APSO for L-PID controllers while NL-PID controller tuned with APSO gives satisfactory dynamic response of the system.

关键词： L-PID NL-PID MOGA APSO Robotic Manipulator

来源：评论

学校读者我要写书评

暂无评论

A Fuzzy Logic Approach to Remaining Useful Life Estimation of Ball Bearings 20th

A Fuzzy Logic Approach to Remaining Useful Life Estimation o...

引用

20th Polish control Conference, PCC 2020

作者： Witczak, Marcin Lipiec, Bogdan Mrugalski, Marcin Stetter, Ralf Institute of Control and Computation Engineering University of Zielona Gora Zielona Gora65-246 Poland Faculty of Mechanical Engineering University of Applied Sciences Ravensburg-Weingarten Weingarten Germany Steinbeis Transfer Center Automotive Systems Ravensburg Germany

ISBN: (纸本)9783030509354

The paper deals with the development of a modelling and prediction scheme capable of estimating a remaining useful life of ball bearings. In particular, a multiple model-based Takagi-Sugeno scheme is developed, which is able to follow the system degradation over the time and predict it in the future. Contrarily to the typical framework, multiple models designed with historical data are used to support diagnostic decisions. In particular, health status determination of the currently operating bearing is supported by the knowledge gathered from the preceding bearings, which went through the run-to-failure process. In both historical and actual bearing cases an efficient modelling scheme with low computational burden is proposed. It is also shown how to exploit it for predicting the bearings remaining useful life. Finally, the proposed approach is applied to data gathered from the PRONOSTIA Platform, designed for the purpose of IEEE Data Challenge pertaining remaining useful life estimation of ball bearings. © 2020, Springer Nature Switzerland AG.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Design and Real Time implementation of fmincon , MOGA tuned IO-PID and FO-P I λ D μ controllers for Stabilization of TRMS

引用

Procedia Computer Science 2020年 171卷 1241-1250页

作者： Sudarshan K. Valluru Rajul Kumar Rahul Kumar Center for Control of Dynamical Systems and Computation Dept. of Electrical Engineering Delhi Technological University Delhi-110042 India

The insistence of this paper is to design Fractional order-proportional Integral Derivative (FO-PID) and Integral order –Proportional Integral Derivative (IO-PID) controllers for trajectory tracking control of Twin rotor Multiple Input Multiple Output (MIMO) system (TRMS). The aim of these designed controllers is to move the main and tail actuator at the desired angles accurately to fulfill the demanding attributes so that TRMS should stabilize while hovering. The controller parameters of both the controllers are optimized using fmincon function as well as Multiobjective genetic algorithm. Both the controller are tested in real time and their performance are compared for control of yaw and pitch angles of TRMS. Manual disturbances are given at after 40 seconds of first given disturbance, and the designed controllers are highly capable in diminishing quickly the effect of manual perturbations given to the main and tail rotor of TRMS. From experiment it is observed from comparative performance that FO-PID controller tuned using multiobjective genetic algorithm give best performance in term of time taken to stabilization after the given disturbance.

关键词： FO-PID IO-PID fmincon MOGA TRMS

来源：评论

学校读者我要写书评

暂无评论

Experimental Investigation of Fully Informed Particle Swarm Optimization tuned Multi Loop L-PID and NL-PID controllers for Gantry Crane System

引用

Procedia Computer Science 2020年 171卷 130-138页

作者： Sudarshan K. Valluru Manpreet Kaur Kumar Kartikeya Arnav Goel Daksh Dobhal Incubation Center for Control Dynamical Systems and Computation Department of Electrical Engineering Delhi Technological University. Delhi-110042 India

This paper presents a novel stabilizing nonlinear PID controller with real time experimental validation on a gantry crane system. Our adaptive control technique describes the underactuated system as a nonlinear mapping of control signal with the hyperbolic tangent function. The proposed function is used to impose a constraint on the proportional, integral and derivative errors such that the system results an effective and robust performance. The main trait of the controller is to bound the high control error to result in a lesser correction value. An enhanced hybrid method based on the bioinspired meta-heuristic algorithm Particle Swarm Optimisation is utilised to tune the gain coefficients K p δ , K I δ and K D δ . The Linear controller and the proposed controllers were compared based on the stabilizing action on the real time system. The real time results depict that the nonlinear PID performs better than the conventional PID.

关键词： PID FIPSO Gantry Crane Hyperbolic Tangent

来源：评论

学校读者我要写书评

暂无评论

Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking control Under Switching Topologies

Cooperative Learning with Gaussian Processes for Euler-Lagra...

引用

American control Conference (ACC)

作者： Zewen Yang Songbo Dong Armin Lederer Xiaobing Dai Siyu Chen Stefan Sosnowski Georges Hattab Sandra Hirche Center for Artificial Intelligence in Public Health Research (ZKI - PH) Robert Koch Institute Berlin Germany Chair of Information-oriented Control (ITR) TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Computer Science ETH Zurich Zurich Switzerland Walker Department of Mechanical Engineering University of Texas at Austin Austin TX USA Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative al-gorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures.

关键词： Uncertainty Protocols Correlation Decentralized control Switches Gaussian processes Topology

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：