• Title/Summary/Keyword: Double regularization

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Double 𝑙1 regularization for moving force identification using response spectrum-based weighted dictionary

  • Yuandong Lei;Bohao Xu;Ling Yu
    • Structural Engineering and Mechanics
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    • v.91 no.2
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    • pp.227-238
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    • 2024
  • Sparse regularization methods have proven effective in addressing the ill-posed equations encountered in moving force identification (MFI). However, the complexity of vehicle loads is often ignored in existing studies aiming at enhancing MFI accuracy. To tackle this issue, a double 𝑙1 regularization method is proposed for MFI based on a response spectrum-based weighted dictionary in this study. Firstly, the relationship between vehicle-induced responses and moving vehicle loads (MVL) is established. The structural responses are then expanded in the frequency domain to obtain the prior knowledge related to MVL and to further construct a response spectrum-based weighted dictionary for MFI with a higher accuracy. Secondly, with the utilization of this weighted dictionary, a double 𝑙1 regularization framework is presented for identifying the static and dynamic components of MVL by the alternating direction method of multipliers (ADMM) method successively. To assess the performance of the proposed method, two different types of MVL, such as composed of trigonometric functions and driven from a 1/4 bridge-vehicle model, are adopted to conduct numerical simulations. Furthermore, a series of MFI experimental verifications are carried out in laboratory. The results shows that the proposed method's higher accuracy and strong robustness to noises compared with other traditional regularization methods.

REGULARITY FOR SOLUTIONS OF FIRST ORDER EVOLUTION EQUATIONS OF VOLTERRA TYPE

  • Jinsoo Hwang
    • East Asian mathematical journal
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    • v.40 no.5
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    • pp.527-549
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    • 2024
  • In this paper we study the semilinear first order evolution problems of Volterra type with Lipschitz continuous nonlinearities. Using the variational formulation of problems due to Dautray and Lions [6], we have proved the fundamental results on existence, uniqueness and continuous dependence of solutions. Especially in the proof of the regularity we have used the double regularization method. Applications to nonlinear partial integro-differential equations are given.

Restoration of Bi-level Images via Iterative Semi-blind Wiener Filtering (반복 semi-blind 위너 필터링을 이용한 이진영상의 복원)

  • Kim, Jeong-Tae
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.7
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    • pp.1290-1294
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    • 2008
  • We present a novel deblurring algorithm for bi-level images blurred by some parameterizable point spread function. The proposed method iteratively searches unknown parameters in the point spread function and noise-to-signal ratio by minimizing an objective function that is based on the binariness and the difference between two intensity values of restoring image. In simulations and experiments, the proposed method showed improved performance compared with the Wiener filtering based method in terms of bit error rate after segmentation.

Hybrid GA-ANN and PSO-ANN methods for accurate prediction of uniaxial compression capacity of CFDST columns

  • Quang-Viet Vu;Sawekchai Tangaramvong;Thu Huynh Van;George Papazafeiropoulos
    • Steel and Composite Structures
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    • v.47 no.6
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    • pp.759-779
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    • 2023
  • The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

Vehicle Face Recognition Algorithm Based on Weighted Nonnegative Matrix Factorization with Double Regularization Terms

  • Shi, Chunhe;Wu, Chengdong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.5
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    • pp.2171-2185
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    • 2020
  • In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

Robust Decoding of Barcode Signals Acquired under Nonuniform Illumination (불균일 조명 하에서 획득된 바코드 신호의 강인 복원)

  • Lee, Han-A;Kim, Jeong-Tae
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.4
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    • pp.718-724
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    • 2008
  • We present a novel joint barcode deblurring and nonuniform illumination compensation algorithm for barcode signals whose number of modules is known. The proposed algorithm is based on a penalized least squares method using a roughness penalty function for an illumination model and a double well penalty function for a barcode signal model. In simulations, the proposed method shows an improved performance compared with a conventional method without compensating nonuniform illumination effects. In addition, the proposed method converges quickly during optimization(within 15 iterations), thereby showing strong possibility for real time decoding of barcode signals.