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randomized algorithms for probabilistic analysis of parametric uncertainties with unmanned helicopters

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 152卷 107459-107459页

作者： Singh, Rupam Bhushan, Bharat Delhi Technol Univ Dept Elect Engn Bawana Rd Delhi India

The unmanned helicopters have grown with wide enthusiasm over the decades due of their benefits with, landing at unprepared sites and terrains, vertical take-off, extensive envelope of flight that varies from cruising to hovering, possibility to fly at short elevations, and very skilful movement in inflexible environments [1].These capabilities resulted in the wide application of aerial vehicles in various zones such as military, civil surveillance, rescue operations, search operations, charting of area, farming claims, building, and bridge constructions inspection, etc. However, the automatic control of the aerial vehicles plays a challenging role as these systems are highly nonlinear and subjected to numerous disturbances. Hence, for a safe operation of the helicopters, and to achieve perfect system management during high turbulence, a significant demand for design of a robust flight control system arises. Generally, the control objective for a helicopter is to track a predetermined trajectory with less settling time and by accu This paper aims at the development of a randomized algorithm based probabilistic analysis approach for parametric uncertainties in unmanned helicopter systems. The proposed approach is developed considering the stochastic characterization of bounded uncertainty in the system assuming that the plant dynamics are exactly known. This provides a new paradigm for synthesizing the controller gain to solve the problem of trajectory tracking for unmanned. Further, to assess the operation of the proposed randomization algorithm-based probabilistic controller and achieve the controller synthesis, a two degrees of freedom (2DoF) helicopter system is modelled and operated with uncertainties for different trajectories. Besides, the robustness of the controller operating under uncertainties is verified with reachability analysis developed on reach tubes and reach sets of the ellipsoidal method. The results identified the efficiency of the proposed appro

关键词： randomized algorithms Probabilistic analysis Parametric uncertainty Helicopter system Reachability analysis

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Non-Intrusive Parametric Reduced Order Modeling using randomized algorithms

Non-Intrusive Parametric Reduced Order Modeling using Random...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Rajaram, Dushhyanth Puranik, Tejas G. Perron, Christian Mavris, Dimitri N. Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn ASDL Atlanta GA 30332 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper demonstrates the creation of purely data-driven, non-intrusive parametric reduced order models (ROMs) for emulation of high-dimensional field outputs using randomized linear algebra techniques. Typically, low-dimensional representations are built using the Proper Orthogonal Decomposition (POD) combined with interpolation/regression in the latent space via machine learning. However, even moderately large simulations can lead to data sets on which the cost of computing the POD becomes intractable due to storage and computational complexity of the numerical procedure. In an attempt to reduce the offline cost, the proposed method demonstrates the application of randomized singular value decomposition (SVD) and sketching-based randomized SVD to compute the POD basis. The predictive capability of ROMs resulting from regular SVD and randomized/sketching-based algorithms are compared with each other to ensure that the decrease in computational cost does not result in a loss in accuracy. Demonstrations on canonical and practical fluid flow problems show that the ROMs resulting from randomized methods are competitive with ROMs that employ the conventional deterministic method. Through this new method, it is hoped that truly large-scale parametric ROMs can be constructed under a significantly limited computational budget.

关键词： randomized algorithms Singular Value Decomposition Computing Machine Learning Fluid Flow Reynolds Averaged Navier Stokes Surrogate Model Angle of Attack Gaussian Process Artificial Neural Network

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Improved randomized algorithms for 3-SAT

Improved Randomized Algorithms for 3-SAT

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21st Annual International Symposium on algorithms and Computations (ISAAC)

作者： Iwama, Kazuo Seto, Kazuhisa Takai, Tadashi Tamaki, Suguru Kyoto Univ Sakyo Ku Kyoto 6068501 Japan East Japan Railway Co Tokyo Japan

ISBN: (纸本)9783642175169

This pager gives a new randomized algorithm which solves 3-SAT in time O(1.32113(n)). The previous best bound is O(1.32216(n)) due to Rolf (J. SAT, 2006). The new algorithm uses the same approach as Iwama and Tamaki (SODA 2004), but exploits the non-uniform initial assignment clue to Hofmeister et al. (STACS 2002) against the Schoning's local search (FOCS 1999).

关键词： 3-SAT exponential-time algorithms randomized algorithms

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Nonlinear System Identification Using Deep Learning and randomized algorithms

Nonlinear System Identification Using Deep Learning and Rand...

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IEEE International Conference on Information and Automation 2015

作者： de la Rosa, Erick Yu, Wen Li, Xiaoou CINVESTAV IPN Natl Polytech Inst Dept Automat Control Mexico City DF Mexico CINVESTAV IPN Natl Polytech Inst Dept Comp Mexico City DF Mexico

ISBN: (纸本)9781467391047

randomized algorithms have good performances for regression and classification problems by using random hidden weights and pseudoinverse computing for the output weights. They have one single hidden layer structure. On the other hand, deep learning techniques have been successfully used for pattern recognition due to their deep structure and effective unsupervised learning. In this paper, the randomized algorithm is modified by the deep learning method. There are multiple hidden layers, and the hidden weights are decided by the input data and modified restricted Boltzmann machines. The output weights are trained by normal randomized algorithms. The proposed deep learning with the randomized algorithms are validated with three benchmark datasets.

关键词： deep learning randomized algorithms system identification

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Bounds for the Number of Tests in Non-adaptive randomized algorithms for Group Testing 46th

Bounds for the Number of Tests in Non-adaptive Randomized Al...

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46th International Conference on Current Trends in Theory and Practice of Informatics (SOFSEM)

作者： Bshouty, Nader H. Haddad, George Haddad-Zaknoon, Catherine A. Technion Haifa Israel Orthodox Arab Coll Grade 11 Haifa Israel

ISBN: (纸本)9783030389192;9783030389185

We study the group testing problem with non-adaptive randomized algorithms. Several models have been discussed in the literature to determine how to randomly choose the tests. For a model M, let m(M)(n, d) be the minimum number of tests required to detect at most d defectives within n items, with success probability at least 1 - delta, for some constant delta. In this paper, we study the measures c(M)(d) = lim(n ->infinity) m(M)(n, d)/ln n and c(M) = lim(d ->infinity) c(M)(d)/d. In the literature, the analyses of such models only give upper bounds for c(M)(d) and c(M), and for some of them, the bounds are not tight. We give new analyses that yield tight bounds for c(M)(d) and c(M) for all the known models M.

关键词： Group testing randomized algorithms Non-adaptive algorithms

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Automated surveillance track filter tuning by randomized algorithms

Automated surveillance track filter tuning by Randomized alg...

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5th International Conference on Information Fusion (FUSION 2002)

作者： Boers, Y Driessen, H Laclé, N THALES NEDERLAND Hengelo Netherlands

ISBN: (纸本)0972184414

Tuning of fine tuning of a tracker system turns out to be a hard job in practice. The main reason for this is that in a practical (surveillance) tracker system there are a lot of design parameters and a lot of competing requirements to be met. This paper provides the user with an algorithm to tune a tracker system automatically and at the same time obtain quantitative results in terms of the optimality of the solution. The theory of randomized algorithms is used to obtain probabilistic statements on the quality, of the output of the tuning process. A simplified example illustrates how the developed theory is to be used.

关键词： target tracking tuning randomized algorithms

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Proofs of randomized algorithms in COQ

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8th International Conference on Mathematics of Program Construction

作者： Audebaud, Philippe Paulin-Mohring, Christine INRIA Saclay Ile France F-91893 Orsay France Ecole Normale Super Lyon LIP F-69437 Lyon France CNRS F-91405 Orsay France Univ Paris Sud LRI F-91405 Orsay France

ISBN: (纸本)3540356312

randomized algorithms are widely used for finding efficiently approximated solutions to complex problems, for instance primality testing and for obtaining good average behavior. Proving properties of such algorithms requires subtle reasoning both on algorithmic and probabilistic aspects of programs. Thus, providing tools for the mechanization of reasoning is an important issue. This paper presents a new method for proving properties of randomized algorithms in a proof assistant based on higher-order logic. It is based on the monadic interpretation of randomized programs as probabilistic distributions (Giry, Ramsey and Pfeffer). It does not require the definition of an operational semantics for the language nor the development of a complex formalization of measure theory. Instead it uses functional and algebraic properties of unit interval. Using this model, we show the validity of general rules for estimating the probability for a randomized algorithm to satisfy specified properties. This approach addresses only discrete distributions and gives rules for analyzing general recursive functions. We apply this theory to the formal proof of a program implementing a Bernoulli distribution from a coin flip and to the (partial) termination of several programs. All the theories and results presented in this paper have been fully formalized and proved in the CoQ proof assistant. (C) 2009 Elsevier B.V. All rights reserved.

关键词： randomized algorithms Proof of partial and total correctness Functional language Axiomatic semantics Probability framing Call-by-value Monadic interpretation

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randomized algorithms for generalized singular value decomposition with application to sensitivity analysis

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NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS 2021年第4期28卷 e2364-e2364页

作者： Saibaba, Arvind K. Hart, Joseph van Bloemen Waanders, Bart North Carolina State Univ Dept Math Box 8205 Raleigh NC 27695 USA Sandia Natl Labs Optimizat & Uncertainty Quantificat POB 5800 Albuquerque NM 87185 USA

The generalized singular value decomposition (GSVD) is a valuable tool that has many applications in computational science. However, computing the GSVD for large-scale problems is challenging. Motivated by applications in hyper-differential sensitivity analysis (HDSA), we propose new randomized algorithms for computing the GSVD which use randomized subspace iteration and weighted QR factorization. Detailed error analysis is given which provides insight into the accuracy of the algorithms and the choice of the algorithmic parameters. We demonstrate the performance of our algorithms on test matrices and a large-scale model problem where HDSA is used to study subsurface flow.

关键词： generalized singular value decomposition iterative methods randomized algorithms sensitivity analysis

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randomized algorithms for total least squares problems

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NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS 2019年第1期26卷 e2219-e2219页

作者： Xie, Pengpeng Xiang, Hua Wei, Yimin Ocean Univ China Sch Math Sci Qingdao 266100 Peoples R China Wuhan Univ Sch Math & Stat Wuhan 430072 Hubei Peoples R China Fudan Univ Key Lab Math Nonlinear Sci Shanghai 200433 Peoples R China

Motivated by the recently popular probabilistic methods for low-rank approximations and randomized algorithms for the least squares problems, we develop randomized algorithms for the total least squares problem with a single right-hand side. We present the Nystrom method for the medium-sized problems. For the large-scale and ill-conditioned cases, we introduce the randomized truncated total least squares with the known or estimated rank as the regularization parameter. We analyze the accuracy of the algorithm randomized truncated total least squares and perform numerical experiments to demonstrate the efficiency of our randomized algorithms. The randomized algorithms can greatly reduce the computational time and still maintain good accuracy with very high probability.

关键词： Golub-Kahan bidiagonalization Lanczos tridiagonalization Nystrom method randomized algorithms singular value decomposition total least squares truncated total least squares

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Weighted randomized algorithms for efficient load balancing in distributed computing environments

Weighted randomized algorithms for efficient load balancing ...

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International Conference on NanoTechnology - Ideas, Innovation and Industries

作者： Hijab, Maniza Damodaram, Avula MJCET CSED Hyderabad India JNTU Sch Informat Technol Hyderabad India

randomized algorithms for resource choice make use of information concerning the spread of their keyed-in data by using random samples. This can effectively improve resource utilization but can create a load imbalance naturally due to the randomness of its input space. For specific problems it is helpful to use a helper to direct the solution space in the right direction. In this paper, to enact effective resource utilization combined with optimized load balancing, a weighted randomized resource assignment algorithm is proposed. The simulation results using standard workload format datasets reveal that the proposed algorithm outperforms existing solutions in average resource utilization by 8% to 12% while improving on load balance by 5% to 11%. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Grid computing Load balancing randomized algorithms Resource utilization Task scheduling

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