Force identification under unknown initial conditions by using concomitant mapping matrix and sparse regularization

2020 ◽  
pp. 107754632094469
Author(s):  
Xijun Ye ◽  
Chudong Pan
Keyword(s):  
Optimization Problem ◽  
Initial Conditions ◽  
Sparse Regularization ◽  
Force Identification ◽  
Moving Force ◽  
Dynamic Forces ◽  
Force Reconstruction ◽  
Structural Responses ◽  
One Step ◽  
Regularization Strategy

Unknown initial conditions can affect the identified accuracy of dynamic forces. Direct measurement of initial conditions is relatively difficult. This study proposes a sparse regularization–based method for identifying forces considering influences of unknown initial conditions. The initial conditions are embedded in a classical governing equation of force identification. The key idea is to introduce a concept of concomitant mapping matrix for reasonably expressing the initial conditions. First, a dictionary is introduced for expanding the dynamic forces. Then, the concomitant mapping matrix is formulated by using free vibrating responses, which correspond to structural responses happening after the structure is subjected to each atom of the force dictionary. A sparse regularization strategy is applied for solving the ill-conditioned equation. After that, the problem of force identification is converted into an optimization problem, and it can be solved by using a one-step strategy. Numerical simulations are carried out for verifying the feasibility and effectiveness of the proposed method. Illustrated results clearly show the applicability and robustness of the proposed method for dealing with force reconstruction and moving force identification.

Moving Force Identification Based on Firefly Algorithm

2014 ◽  
Vol 919-921 ◽  
pp. 329-333 ◽  
Author(s):  
Chu Dong Pan ◽  
Ling Yu
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problem ◽  
Structural Response ◽  
Strain Sensor ◽  
Constrained Optimization Problem ◽  
Force Identification ◽  
Moving Force ◽  
Structural Responses ◽  
Novel Method ◽  
Truss Bridge

Based on firefly algorithm (FA), a novel method is proposed for moving force identification (MFI) in this paper. The basic principle of FA is introduced, some key parameters, such as light intensity, attractiveness, and the rules of movement are defined. The inverse problem on MFI is transformed into a constrained optimization problem and then can be hopefully solved by FA. In order to assess the accuracy and the feasibility of the proposed method, a beam-like truss bridge subjected to moving forces is taken an example for numerical simulation. The robustness of the method is also evaluated by adding noise into the structural responses. The illustrated results show that the proposed method can accurately identify two moving forces from incomplete structural response at only one strain sensor with stronger robustness.

Moving force identification based on sparse regularization combined with moving average constraint

2021 ◽  
Vol 515 ◽  
pp. 116496
Author(s):  
Chudong Pan ◽  
Zhenjie Huang ◽  
Junda You ◽  
Yisha Li ◽  
Lihua Yang
Keyword(s):  
Moving Average ◽  
Sparse Regularization ◽  
Force Identification ◽  
Moving Force

Constrained Dual-Level Bandit for Personalized Impression Regulation in Online Ranking Systems

2021 ◽  
Vol 16 (2) ◽  
pp. 1-23
Author(s):  
Zhao Li ◽  
Junshuai Song ◽  
Zehong Hu ◽  
Zhen Wang ◽  
Jun Gao
Keyword(s):  
Optimization Problem ◽  
Initial Conditions ◽  
Global Scale ◽  
Global Constraints ◽  
Economic Losses ◽  
Second Order Cone ◽  
Ranking Systems ◽  
Item Level ◽  
Shopping Experience ◽  
Regulation Method

Impression regulation plays an important role in various online ranking systems, e.g. , e-commerce ranking systems always need to achieve local commercial demands on some pre-labeled target items like fresh item cultivation and fraudulent item counteracting while maximizing its global revenue. However, local impression regulation may cause “butterfly effects” on the global scale, e.g. , in e-commerce, the price preference fluctuation in initial conditions (overpriced or underpriced items) may create a significantly different outcome, thus affecting shopping experience and bringing economic losses to platforms. To prevent “butterfly effects”, some researchers define their regulation objectives with global constraints, by using contextual bandit at the page-level that requires all items on one page sharing the same regulation action, which fails to conduct impression regulation on individual items. To address this problem, in this article, we propose a personalized impression regulation method that can directly makes regulation decisions for each user-item pair. Specifically, we model the regulation problem as a C onstrained D ual-level B andit (CDB) problem, where the local regulation action and reward signals are at the item-level while the global effect constraint on the platform impression can be calculated at the page-level only. To handle the asynchronous signals, we first expand the page-level constraint to the item-level and then derive the policy updating as a second-order cone optimization problem. Our CDB approaches the optimal policy by iteratively solving the optimization problem. Experiments are performed on both offline and online datasets, and the results, theoretically and empirically, demonstrate CDB outperforms state-of-the-art algorithms.

Moving force identification based on redundant concatenated dictionary and weighted l1-norm regularization

2018 ◽  
Vol 98 ◽  
pp. 32-49 ◽  
Author(s):  
Chu-Dong Pan ◽  
Ling Yu ◽  
Huan-Lin Liu ◽  
Ze-Peng Chen ◽  
Wen-Feng Luo
Keyword(s):  
L1 Norm ◽  
Force Identification ◽  
Moving Force

scholarly journals An Innovative Optimization Design Procedure for Mechatronic Systems with a Multi-Criteria Formulation

2021 ◽  
Vol 11 (19) ◽  
pp. 8900
Author(s):  
Cuauhtémoc Morales-Cruz ◽  
Marco Ceccarelli ◽  
Edgar Alfredo Portilla-Flores
Keyword(s):  
Optimization Design ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Design Procedure ◽  
Concurrent Design ◽  
Mechatronic Systems ◽  
Design Variables ◽  
One Step ◽  
Task Oriented ◽  
And Task

This paper presents an innovative Mechatronic Concurrent Design procedure to address multidisciplinary issues in Mechatronics systems that can concurrently include traditional and new aspects. This approach considers multiple criteria and design variables such as mechanical aspects, control issues, and task-oriented features to formulate a concurrent design optimization problem that is solved using but not limited to heuristic algorithms. Furthermore, as an innovation, this procedure address all considered aspects in one step instead of multiple sequential stages. Finally, this work discusses an example referring to Mechatronic Design to show the procedure performed and the results show its capability.

scholarly journals Disease control as an optimization problem

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257958
Author(s):  
Miguel Navascués ◽  
Costantino Budroni ◽  
Yelena Guryanova
Keyword(s):  
Disease Control ◽  
Stochastic Models ◽  
Optimization Problem ◽  
Initial Conditions ◽  
Optimization Theory ◽  
Model Parameters ◽  
Grid Search ◽  
Brute Force ◽  
Time Required ◽  
Policy Optimization

In the context of epidemiology, policies for disease control are often devised through a mixture of intuition and brute-force, whereby the set of logically conceivable policies is narrowed down to a small family described by a few parameters, following which linearization or grid search is used to identify the optimal policy within the set. This scheme runs the risk of leaving out more complex (and perhaps counter-intuitive) policies for disease control that could tackle the disease more efficiently. In this article, we use techniques from convex optimization theory and machine learning to conduct optimizations over disease policies described by hundreds of parameters. In contrast to past approaches for policy optimization based on control theory, our framework can deal with arbitrary uncertainties on the initial conditions and model parameters controlling the spread of the disease, and stochastic models. In addition, our methods allow for optimization over policies which remain constant over weekly periods, specified by either continuous or discrete (e.g.: lockdown on/off) government measures. We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020).

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