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IEEE Annual Conference on Decision and Control

作者： Yueqian Liang Yingmin Jia Junping Du Fumitoshi Matsuno Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China Department of Mechanical Engineering and Science Kyoto University Kyoto 606-8501 Japan

ISBN: (纸本)9781467360890

This paper investigates the problem of tracking a moving target using multiple fixed-wing unmanned aerial vehicles (UAVs) under a new bicircular formation. This new formation for cooperative standoff target tracking is first developed by maximizing the amount of Fisher information. Guidance vector fields are designed, and based on them, a cooperation approach is proposed to adapt the unknown target velocity and achieve the cooperative bicircular tracking. Speed and heading rate are firstly controlled to track the guidance vector fields separately for each UAV, then the speed is further adjusted to maintain the desired angular separation between the UAVs. Simulation experiments are given to verify the effectiveness of the proposed approach.

关键词： bicircular Target tracking vehicle

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Output feedback tracking control for spacecraft relative translation subject to input constraints and partial loss of control effectiveness

Output feedback tracking control for spacecraft relative tra...

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American Control Conference

作者： Lin Zhao Yingmin Jia Junping Du Jun Zhang Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electronic and Information Engineering Beihang University (BUAA) Beijing 100191 China

ISBN: (纸本)9781479932757

In this paper, an adaptive output feedback tracking control scheme is proposed for spacecraft formation flying (SFF) in the presence of external disturbances, uncertain system parameters, input constraints and partial loss of control effectiveness. The proposed controller incorporates a pseudo-velocity filter to account for the unmeasured relative velocity, and the neural network (NN) technique is implemented to approximate the desired nonlinear function and bounded external disturbances. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated nonlinear function, a switch function is employed to generate a switching between the adaptive NN control and the robust controller. Moreover, a fault tolerant part is included in the controller to compensate the partial loss of actuator effectiveness fault. It is shown that the derived controller not only guarantees the tracking error in the closed-loop system to be uniformly ultimately bounded (UUB) but also ensures the control input can rigorously enforce actuator magnitude constraints. Simulation results are provided to demonstrate the effectiveness of the proposed method.

关键词： SFF UUB NN

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Real-time embedded system support for the BTeV level 1 muon trigger

Real-time embedded system support for the BTeV level 1 muon ...

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2003 IEEE Nuclear Science Symposium Conference Record - Nuclear Science Symposium, Medical Imaging Conference

作者： Haney, Michael J. Beauregard, D. Iyer, R. Kalbarczyk, Z. Liu, Q. Wang, L. Haney, M. Selen, M. Mosse, D. Shigiltchoff, O. Chopade, R. Hovey, L. Jung, M. Messie, D. Oh, J. Stone, S. Bapty, T. Neema, S. Nordstrom, S. Shetty, S. Vashishtha, S. Yao, D. Sheldon, P. Vaandering, E. Butler, J. Gottschalk, E. Kowalkowski, J. Votava, M. Appel, J. University of Illinois Urbana IL 61801 United States Des./Validation Reliable N.S.R.G. University of Illinois High Energy Physics Group Department of Physics University of Illinois Department of Computer Science Coll. in High Sch. Educ. Program University of Pittsburgh Department of Electrical Engineering Syracuse University High Energy Physics Group Department of Physics Syracuse University Inst. for Software Intgd. Systems Vanderbilt University Department of Physics BTeV Group Vanderbilt University BTeV Collaboration Particle Physics Division Fermi Natl. Accelerator Laboratory Computing Division Fermi Natl. Accelerator Laboratory Fermilab Education Office Fermi Natl. Accelerator Laboratory

The Level 1 Muon Trigger subsystem for BTeV will be implemented using the same architectural building blocks as the BTeV Level 1 Pixel Trigger: pipelined field programmable gate arrays feeding a farm of dedicated processing elements. The muon trigger algorithm identifies candidate tracks, and is sensitive to the muon charge (sign);candidate dimuon events are identified by complementary charge track-pairs. To insure that the trigger is operating effectively, the trigger development team is actively collaborating in an independent multi-university research program for reliable, self-aware, fault adaptive behavior in real-time embedded systems (RTES). Key elements of the architecture, algorithm, performance, and engineered reliability are presented.

关键词： Embedded systems

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Stochastic H_2 Optimal Control of Discrete-Time Markov Jump systems with Periodic Coefficients

Stochastic H_2 Optimal Control of Discrete-Time Markov Jump ...

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American Control Conference

作者： Hongji Ma Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781457710957

In this paper, the H_2 optimal state-feedback control problem is investigated for discrete-time Markov jump systems subject to periodic coefficients and multiplicative noise. First of all, an exhaustive analysis is pursued to set up an H_2 performance criterion (H_2 norm), which can be characterized by the periodic solutions of two generalized Lyapunov equations. Based on the obtained H_2 norm, the H_2 optimal state-feedback controller is designed in terms of the stabilizing solution of a discrete-time generalized Riccati equation with periodic coefficients. Moreover, this stabilizing solution can be computed efficiently by the available iterative algorithm.

关键词： Markov processes Noise Educational institutions Riccati equations Optimal control

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Security analysis and improvement of hsu et al.'s threshold proxy signature scheme

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International Journal of Network Security 2006年第1期2卷 69-72页

作者： Guo, Lifeng Cheng, Xiangguo Liu, Yang Institute of Systems Science Academy of Mathematics and System Sciences Chinese Academy of Sciences Graduate School of Chinese Academy of Sciences Beijing 100080 China 21 Heng Mui Keng Terrace Singapore State Key Laboratory of Integrated Services Network Xidian University Xi'an 710071 China Software Institute School of Electronic Engineering and Computer Science Peking University 100871 China

In 1999, Sun et al. proposed a new (t, n) threshold proxy signature scheme based on Zhang's threshold proxy signature scheme. But in 2003 Hsu et al. pointed out that Sun's scheme suffered from a drawback and demonstrated an improvement to counter it. In this paper we point out that Hsu's scheme suffers from an insider attack against their scheme. That is, a malicious proxy signer can forge a valid threshold proxy signature on any message. To thwart this attack, some improvements are further proposed.

关键词： Authentication

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State estimation for jump Markov linear systems with uncompensated biases

State estimation for jump Markov linear systems with uncompe...

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American Control Conference

作者： Wenling Li Yingmin Jia Junping Du Jun Zhang Deyuan Meng Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electronic and Information Engineering Beihang University (BUAA) Beijing 100191 China

ISBN: (纸本)9781479901777

This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has been developed by applying the basic interacting multiple model (IMM) approach. To derive a precise representation of the biases contributions to the state estimation, three auxiliary matrices are introduced with respect to the correlation between the state estimation errors and the biases, which helps to derive mode-conditioned estimates in the framework of the IMM. A numerical example involving tracking a maneuvering target is provided to compare the performance of the proposed filter with that of the augmented state filter.

关键词： Jump Markov linear system Interacting multiple model Uncompensated bias State estimation Bias uncompensated maneuvering target Linear system interacting boson model Markov jump IMM

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

arXiv

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arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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Kalman filtering with multiple nonlinear-linear mixing state constraints

Kalman filtering with multiple nonlinear-linear mixing state...

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IEEE Conference on Decision and Control

作者： X. Fu Y. Jia J. Du F. Yu Seventh Research Division and the Department of Systems and Control Beihang University Beijing China Beihang University Beijing CN Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

This paper is devoted to the problem of state estimation for a class of dynamic system with multiple nonlinear-linear (NL) mixing state constraints. A computational algorithm was developed by C. Yang and E. Blasch to incorporate a positive definite quadratic form equality constraint into the Kalman filtering for the ground vehicle tracking. However, when the target tracked in multidimensional space, a single positive definite quadratic form constraint is not enough due to the involvement of other surface such as hyperboloid, paraboloid, or even plane. A analytic method of incorporating multiple nonlinear-linear mixing state constraints into the Kalman filter is presented here, and a sufficient condition is obtained that guarantee the existence of solution. The constraints may be time varying. At each time step the unconstrained Kalman filter solution is projected onto the state constraints surface. This significantly improves the prediction accuracy of the filter. The use of this algorithm is demonstrated on a nonlinear-linear mixing spacecraft positioning problem.

关键词： Kalman filters Symmetric matrices State estimation Optimization Covariance matrix Noise

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Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting

arXiv

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arXiv 2022年

作者： Xing, Wei W. Zhang, Ziyang Shah, Akeel A. Key Laboratory of Low-grade Energy Utilisation Technologies and Systems MOE Chongqing University Chongqing400030 China School of Mathematics and Statistics University of Sheffield Hounsfield Road SheffieldS3 7RH United Kingdom College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health during repeated cycling. To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems. Unless multiple battery data sets are used for a direct prediction of the end-of-life, which is useful for ball-park estimates, such an approach is infeasible since the features are not known for future cycles. In this paper, we develop a highly-accurate method that can overcome this limitation, by using a modified Gaussian process dynamical model (GPDM). We introduce a kernelised version of GPDM for a more expressive covariance structure between both the observable and latent coordinates. We combine the approach with transfer learning to track the future state-of-health up to end-of-life. The method can incorporate features as different physical observables, without requiring their values beyond the time up to which data is available. Transfer learning is used to improve learning of the hyperparameters using data from similar batteries. The accuracy and superiority of the approach over modern benchmarks algorithms including a Gaussian process model and deep convolutional and recurrent networks is demonstrated on three data sets, particularly at the early stages of battery lifetime. Copyright © 2022, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

arXiv

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arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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