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Realization algorithms for expansions in generalized orthonormal basis functions

IFAC Proceedings Volumes 1999年第2期32卷 4153-4158页

作者： P.S.C. Heuberger Z. Szabó T.J. de Hoog P.M.J. Van den Hof J. Bokor Mechanical Engineering Systems and Control Group Delft University of Technology Mekelweg 2 2628 CD Delft. The Netherlands Computer and Automation Research Institute Hungarian Academy of Sciences Kende u. 13-17 H 1502 Budapest Hungary

In this paper a realization theory and associated algorithms are presented for the construction of minimal realizations on the basis of a sequence of expansion coefficients in a generalized orthonormal basis. Both the exact and the partial realization problem are addressed and solved, leading to extended versions of the classical Ho-Kalman *** the construction of the realization algorithms, fruitful use is made of a system analysis in the transform domain, being induced by the choice of basis functions. The resulting algorithms can also be applied in approximate realization and in system approximation.

关键词： Orthogonal basis functions discrete-time systems system approximation realization Identification

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Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identification

Extended Kalman Filtering with Low-Rank Tensor Networks for ...

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IEEE Annual Conference on Decision and control

作者： Kim Batselier Ching-Yun Ko Ngai Wong Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This article reformulates the multiple-input-multiple-output Volterra system identification problem as an extended Kalman filtering problem. This reformulation has two advantages. First, it results in a simplification of the solution compared to the Tensor Network Kalman filter as no tensor filtering equations are required anymore. The second advantage is that the reformulation allows to model correlations between the parameters of different multiple-input-single-output Volterra systems, which can lead to better accuracy. The curse of dimensionality in the exponentially large parameter vector and covariance matrix is lifted through the use of low-rank tensor networks. The computational complexity of our tensor network implementation is compared to the conventional implementation and numerical experiments demonstrate the effectiveness of the proposed method.

关键词： Tensile stress Mathematical model Covariance matrices Kalman filters MIMO communication Noise measurement MISO communication

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Stick-slip oscillations in resonant power converters

Stick-slip oscillations in resonant power converters

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： M. di Bernardo E. Fossas Department of Systems and Computer Science University of Naples Federico II Naples Italy Department of Engineering Mathematics University of Bristol Bristol U.K. Institute of Industrial and Control Engineering Universitat Politecnica de Catalunya Barcelona Spain

The paper presents evidence of the existence of stick-slip oscillations, usually found in mechanical systems with friction, in a class of resonant power converters. It is shown that these converters can exhibit periodic solutions characterized by segments of sliding motion (associated to theoretically infinitely many switchings). Also, numerical and analytical evidence of the occurrence of sliding bifurcations is given. These phenomena have recently been presented in the literature and the paper reports their occurrence in power electronics for the first time.

关键词： Resonance Bifurcation Partial response channels Friction Power electronics Switching converters Oscillators Orbits Voltage Limit-cycles

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Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

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GBMIA: Gradient-based Membership Inference Attack in Federated Learning

GBMIA: Gradient-based Membership Inference Attack in Federat...

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IEEE International Conference on Communications (ICC)

作者： Xiaodong Wang Naiyu Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Mathematics & Computer Science Fayetteville State University Fayetteville NC USA Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

Membership inference attack (MIA) has been proved to pose a serious threat to federated learning (FL). However, most of the existing membership inference attacks against FL rely on the specific attack models built from the target model behaviors, which make the attacks costly and complicated. In addition, directly adopting the inference attacks that are originally designed for machine learning models into the federated scenarios can lead to poor performance. We propose GBMIA, an attack model-free membership inference method based on gradient. We take full advantage of the federated learning process by observing the target model's behaviors after gradient ascent tuning. And we combine prediction correctness and the gradient norm-based metric for membership inference. The proposed GBMIA can be conducted by both global and local attackers. We conduct experimental evaluations on three real-world datasets to demonstrate that GBMIA can achieve a high attack accuracy. We further apply the arbitration mechanism to increase the effectiveness of GBMIA which can lead to an attack accuracy close to 1 on all three datasets. We also conduct experiments to substantiate that clients going offline and the overlap of clients' training sets have great effect on the membership leakage in FL.

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Evaluating the Predictive Ability of the Bipartite Dengue Contact Network Model

Evaluating the Predictive Ability of the Bipartite Dengue Co...

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2022 IEEE International Conference on Computing, ICOCO 2022

作者： Labadin, Jane Hong, Boon Hao Tiong, Wei King Gill, Balvinder Singh Perera, David Rigit, Andrew Singh, Sarbhan Vei, Tan Cia Ghazali, Sumarni Mohd Jelip, Jenarun Mokhtar, Norhayati Keong, Wan Ming University Malaysia Sarawak Faculty of Computer Science and Information Technology Sarawak Kota Samarahan Malaysia Ministry of Health Malaysia Malaysia Institute for Health and Community Medicine Universiti Malaysia Sarawak Kota Samarahan94300 Malaysia Faculty of Engineering Universiti Malaysia Sarawak Kota Samarahan94300 Malaysia Ministry of Health Malaysia Vector Borne Disease Control Division Putrajaya62000 Malaysia

ISBN: (纸本)9781665489966

This paper presents the predictive power analysis of the bipartite dengue contact (BDC) network model for identifying the source of dengue infection, defined as dengue hotspot. This BDC network model was earlier formulated, verified and validated using data collected in Sarawak, Malaysia. Then, a web-based BDC network system was implemented and subsequently tested by 7 other areas in Malaysia. The data collected using the system was then used to further evaluate the predictive ability of the BDC network model. The validity period of the dengue hotspots identified by the BDC network model was measured based on the accuracy of the predictive power analysis and Spearman's Rank Correlation Coefficient (SRCC). Based on the results, using prior one-week data was sufficient to predict the dengue hotspot for the following week and subsequent two weeks. This shows that the hotspots are valid for two weeks. The accuracy for the outbreak areas is above 60%. Most of the model reported an SRCC above 0.70 which indicated a strong positive relationship between the hotspots in the targeted model and the validated model. Due to the accuracy and SRCC values obtained, it is suggested that the BDC network model can proceed further with retrospective data for other dengue outbreak areas in Malaysia and a prospective study for the areas that participated in this study. © 2022 IEEE.

关键词： accuracy Malaysia predictive ability ranking source of infection

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ESAS: An efficient semantic and authorized search scheme over encrypted outsourced data

arXiv

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arXiv 2018年

作者： Liu, Xueyan Guan, Zhitao Du, Xiaojiang Zhu, Liehuang Yu, Zhengtao Ma, Yinglong School of Control and Computer Engineering North China Electric Power University Beijing China Department Zhitao Guan1 of Computer and Information Sciences Temple University PhiladelphiaPA United States School of Computer Beijing Institute of Technology Beijing China School of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan China

Nowadays, a large amount of user privacysensitive data is outsourced to the cloud server in ciphertext, which is provided by the data owners and can be accessed by authorized data users. When accessing data, the user should be assigned with the access permission according to his identities or attributes. In addition, the search capabilities in encrypted outsourced data is expected to be enhanced, i.e., the search results can better present user's intentions. To address the above issues, ESAS, an Efficient Semantic and Authorized Search scheme over encrypted outsourced data, is proposed. In ESAS, by integrating PRSCG (the privacy-preserving ranked search based on conceptual graph) and CP-ABE (ciphertext policy attribute-based encryption), semantic search with file-level fine-grained access authorization can be realized. In addition, search authorization can be done in an offline manner, which can improve search efficiency and reduce the response time. The security analysis indicate that the proposed ESAS meets security requirement. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantic Web

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A New Model for Time-delay Systems with Application to Network Based control

A New Model for Time-delay Systems with Application to Netwo...

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Chinese control Conference (CCC)

作者： Huijun Gao Tongwen Chen James Lam Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Mechanical Engineering University of Hong Kong Hong Kong China

This paper proposes a new model for time-delay systems, which contains multiple successive delay components in the state and has important applications in remote control and network based control. New results on stability and H ∞ performance are obtained for systems with two successive delay components. The second part of this paper applies the proposed new model to network based control, which has emerged as a topic of significant interest in the control community. A sampled-data networked control system with simultaneous consideration of network induced delays, data packet dropouts and measurement quantization is modelled as a time-delay system with two successive delay components in the state and, the problem of network based H ∞ control is solved accordingly.

关键词： Stability Space technology Delay effects Delay systems Application software Networked control systems Quantization Differential equations Solid modeling Mechanical engineering

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Nonlinear modeling of a flight vehicle using fuzzy clustering

Nonlinear modeling of a flight vehicle using fuzzy clusterin...

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IEEE International Conference on Granular Computing (GRC)

作者： A.R. Mehrabian S.V. Hashemi J. Roshanian S. Mohagheghi The Control and Intelligence Processing Center of Excellence University of Tehran Iran Department of AeroSpace Engineering K.N. Toosi University of Technology Tehran Iran School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

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Dynamic vehicle routing for robotic systems

Dynamic vehicle routing for robotic systems

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作者： Bullo, Francesco Frazzoli, Emilio Pavone, Marco Savla, Ketan Smith, Stephen L. Center for Control Dynamical Systems and Computation Department of Mechanical Engineering University of California in Santa Barbara Santa Barbara CA 93106 United States Laboratory for Information and Decision Systems Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA 02139 United States Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA 02139 United States Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

Recent years have witnessed great advancements in the science and technology of autonomy, robotics, and networking. This paper surveys recent concepts and algorithms for dynamic vehicle routing (DVR), that is, for the automatic planning of optimal multivehicle routes to perform tasks that are generated over time by an exogenous process. We consider a rich variety of scenarios relevant for robotic applications. We begin by reviewing the basic DVR problem: demands for service arrive at random locations at random times and a vehicle travels to provide on-site service while minimizing the expected wait time of the demands. Next, we treat different multivehicle scenarios based on different models for demands (e.g., demands with different priority levels and impatient demands), vehicles (e.g., motion constraints, communication, and sensing capabilities), and tasks. The performance criterion used in these scenarios is either the expected wait time of the demands or the fraction of demands serviced successfully. In each specific DVR scenario, we adopt a rigorous technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. First, we establish fundamental limits on the achievable performance, including limits on stability and quality of service. Second, we design algorithms, and provide provable guarantees on their performance with respect to the fundamental limits. © 2011 IEEE.

关键词： Adaptive algorithms

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