• Title/Summary/Keyword: randomized algorithm

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ILL-VERSUS WELL-POSED SINGULAR LINEAR SYSTEMS: SCOPE OF RANDOMIZED ALGORITHMS

  • Sen, S.K.;Agarwal, Ravi P.;Shaykhian, Gholam Ali
    • Journal of applied mathematics & informatics
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    • v.27 no.3_4
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    • pp.621-638
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    • 2009
  • The linear system Ax = b will have (i) no solution, (ii) only one non-trivial (trivial) solution, or (iii) infinity of solutions. Our focus will be on cases (ii) and (iii). The mathematical models of many real-world problems give rise to (a) ill-conditioned linear systems, (b) singular linear systems (A is singular with all its linearly independent rows are sufficiently linearly independent), or (c) ill-conditioned singular linear systems (A is singular with some or all of its strictly linearly independent rows are near-linearly dependent). This article highlights the scope and need of a randomized algorithm for ill-conditioned/singular systems when a reasonably narrow domain of a solution vector is specified. Further, it stresses that with the increasing computing power, the importance of randomized algorithms is also increasing. It also points out that, for many optimization linear/nonlinear problems, randomized algorithms are increasingly dominating the deterministic approaches and, for some problems such as the traveling salesman problem, randomized algorithms are the only alternatives.

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Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

  • Safarinejadian, Behrouz;Vafamand, Navid
    • Journal of Electrical Engineering and Technology
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    • v.10 no.3
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    • pp.1212-1220
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    • 2015
  • This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.

A Novel Redundant Data Storage Algorithm Based on Minimum Spanning Tree and Quasi-randomized Matrix

  • Wang, Jun;Yi, Qiong;Chen, Yunfei;Wang, Yue
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.1
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    • pp.227-247
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    • 2018
  • For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

Bayesian Analysis of Randomized Response Models : A Gibbs Sampling Approach

  • Oh, Man-Suk
    • Journal of the Korean Statistical Society
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    • v.23 no.2
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    • pp.463-482
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    • 1994
  • In Bayesian analysis of randomized response models, the likelihood function does not combine tractably with typical priors for the parameters of interest, causing computational difficulties in posterior analysis of the parameters of interest. In this article, the difficulties are solved by introducing appropriate latent variables to the model and using the Gibbs sampling algorithm.

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Construction of Efficient and Secure Pairing Algorithm and Its Application

  • Choi, Doo-Ho;Han, Dong-Guk;Kim, Ho-Won
    • Journal of Communications and Networks
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    • v.10 no.4
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    • pp.437-443
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    • 2008
  • The randomized projective coordinate (RPC) method applied to a pairing computation algorithm is a good solution that provides an efficient countermeasure against side channel attacks. In this study, we investigate measures for increasing the efficiency of the RPC-based countermeasures and construct a method that provides an efficient RPC-based countermeasure against side channel attacks. We then apply our method to the well-known $\eta_T$ pairing algorithm over binary fields and obtain an RPC-based countermeasure for the $\eta_T$ pairing; our method is more efficient than the RPC method applied to the original $\eta_T$ pairing algorithm.

A SUCCESSIVE QUADRATIC PROGRAMMING ALGORITHM FOR SDP RELAXATION OF THE BINARY QUADRATIC PROGRAMMING

  • MU XUEWEN;LID SANYANG;ZHANG YALING
    • Bulletin of the Korean Mathematical Society
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    • v.42 no.4
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    • pp.837-849
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    • 2005
  • In this paper, we obtain a successive quadratic programming algorithm for solving the semidefinite programming (SDP) relaxation of the binary quadratic programming. Combining with a randomized method of Goemans and Williamson, it provides an efficient approximation for the binary quadratic programming. Furthermore, its convergence result is given. At last, We report some numerical examples to compare our method with the interior-point method on Maxcut problem.

Line Segment Based Randomized Hough Transform (선분 세그먼트 기반 Randomized Hough Transform)

  • Hahn, Kwang-Soo;Han, Young-Joon;Hahn, Hern-Soo
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.44 no.6
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    • pp.11-20
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    • 2007
  • This paper proposes a new efficient method to detect ellipses using a segment merging based Randomized Hough Transform. The key idea of the proposed method is to separate single line segments from an edge image, to estimate ellipses from any pair of the single line segments using Randomized Hough Transform (RHT), and to merge the ellipses. This algorithm is able to accuracy estimate the number of ellipses and largely improves the computational time by reducing iterations.

Robot Control via RPO-based Reinforcement Learning Algorithm (RPO 기반 강화학습 알고리즘을 이용한 로봇제어)

  • Kim, Jong-Ho;Kang, Dae-Sung;Park, Joo-Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.4
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    • pp.505-510
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    • 2005
  • The RPO(randomized policy optimizer) algorithm, which utilizes probabilistic policy for the action selection, is a recently developed tool in the area of reinforcement learning, and has been shown to be very successful in several application problems. In this paper, we propose a modified RPO algorithm, whose critic network is adapted via RLS(Recursive Least Square) algorithm. In order to illustrate the applicability of the modified RPO method, we applied the modified algorithm to Kimura's robot and observed very good performance. We also developed a MATLAB-based animation program, by which the effectiveness of the training algorithms on the acceleration or the robot movement were observed.

Distributed Scheduling Scheme for Optimal Performance in Wireless Networks

  • Tran, Nguyen H.;Hong, Choong-Seon
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06d
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    • pp.232-233
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    • 2011
  • We propose a randomized distributed scheduling algorithm which can achieve the optimal throughput under the general interference model. The proposed algorithm is analyzed to show an attractive performance in that it can return a maximal schedule with high probability and has a low time-complexity. We also provide the simulation results to validate performance analysis of our algorithm.

Randomization of Elliptic Curve Secret Key to Efficiently Resist Power Analysis (전력분석공격을 효율적으로 방어하는 타원곡선 비밀키의 랜덤화)

  • 장상운;정석원;박영호
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.13 no.5
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    • pp.169-177
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    • 2003
  • We establish the security requirements and derive a generic condition of elliptic curve scalar multiplication to resist against DPA and Goubin’s attack. Also we show that if a scalar multiplication algorithm satisfies our generic condition, then both attacks are infeasible. Showing that the randomized signed scalar multiplication using Ha-Moon's receding algorithm satisfies the generic condition, we recommend the randomized signed scalar multiplication using Ha-Moon's receding algorithm to be protective against both attacks. Also we newly design a random recoding method to Prevent two attacks. Finally, in efficiency comparison, it is shown that the recommended method is a bit faster than Izu-Takagi’s method which uses Montgomery-ladder without computing y-coordinate combined with randomized projective coordinates and base point blinding or isogeny method. Moreover. Izu-Takagi’s method uses additional storage, but it is not the case of ours.