Background:In magnetic resonance imaging (MRI), maintaining a highly uni-form main magnetic field (B0) is essential for producing detailed images ofhuman *** shimming (PS) is a technique used to enhanceB0uni-formity b...
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Background:In magnetic resonance imaging (MRI), maintaining a highly uni-form main magnetic field (B0) is essential for producing detailed images ofhuman *** shimming (PS) is a technique used to enhanceB0uni-formity by strategically arranging shimming iron pieces inside the magnet ***, PS optimization has been implemented using linear programming(LP), posing challenges in balancing field quality with the quantity of iron usedfor shimming. Purpose:In this work, we aimed to improve the efficacy of passive shimmingthat has the advantages of balancing field quality,iron usage,and harmonics inan optimal manner and leads to a smoother field profile. Methods:This study introduces a hybrid algorithm that combines parti-cle swarm optimization with sequential quadratic programming (PSO-SQP)to enhance shimming performance. Additionally, a regularization method isemployed to reduce the iron pieces' weight effectively. Results:The simulation study demonstrated that the magnetic field wasimproved from 462 to 3.6 ppm,utilizing merely 1.2 kg of iron in a 40 cm diameterspherical volume (DSV) of a 7T MRI magnet. Compared to traditional opti-mization techniques, this method notably enhanced magnetic field uniformityby 96.7% and reduced the iron weight requirement by 81.8%. Conclusion:The results indicated that the proposed method is expected to beeffective for passive shimming
In this paper, we present and describe a computationally efficient sequential l(1) quadraticprogramming (Sl(1)QP) algorithm for Nonlinear Model Predictive Control (NMPC). We use a tailored trust region sequential qua...
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In this paper, we present and describe a computationally efficient sequential l(1) quadraticprogramming (Sl(1)QP) algorithm for Nonlinear Model Predictive Control (NMPC). We use a tailored trust region sequential quadratic programming for the solution of the optimal control problem (OCP) involved in the NMPC algorithm. We use a multiple shooting approach for numerical integration and sensitivity computation. A second order correction ensures a faster convergence of the SQP algorithm. We exploit the structure of the OCP by using an efficient primal-dual interior point algorithm based on Riccati factorizations and a block diagonal BFGS update of the Hessian matrix. The complexity scales linearly with the prediction horizon length. We numerically evaluate and compare the performance of our algorithm on a numerical example. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Locomotives are susceptible to wheel slip when passing through the low adhesion sections. Wheel slip is more likely to occur on wheelsets with reduced axle load due to axle load transfer (ALT). To address this problem...
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Locomotives are susceptible to wheel slip when passing through the low adhesion sections. Wheel slip is more likely to occur on wheelsets with reduced axle load due to axle load transfer (ALT). To address this problem, we propose a nonlinear optimisation method for traction forces to improve the locomotive adhesion utilisation under the rail adhesion limitation. Firstly, we established an improved ALT model considering the rotation of the traction rod and verified its effectiveness through a field test. Then, we applied a sequential quadratic programming algorithm to optimise the traction forces with the constraints of the adhesion and ALT. Based on the proposed method, we analysed the impact of static axle load, traction force limitations, and coupler pitch angle on the optimisation results. The results indicate that the optimisation method can effectively utilize the axle load and adhesion of all wheelsets under different factors. A train dynamics model incorporating the optimisation module is further developed to investigate the method's effectiveness in improving the adhesion utilisation of locomotives. Simulation results demonstrate that the proposed optimisation method can enhance the maximum traction force of the locomotive, effectively preventing the wheelset with reduced axle load from slipping.
作者：
Al-Homidan, SulimanKFUPM
Dept Math Dhahran 31261 Saudi Arabia KFUPM
Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia
The task of deducing directed acyclic graphs from observational data has gained significant attention recently due to its broad applicability. Consequently, connecting the log-det characterization domain with the set ...
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The task of deducing directed acyclic graphs from observational data has gained significant attention recently due to its broad applicability. Consequently, connecting the log-det characterization domain with the set of M-matrices defined over the cone of positive definite matrices has emerged as a crucial approach in this field. However, experimentally collected data often deviates from the expected positive semidefinite structure due to introduced noise, posing a challenge in maintaining its physical structure. In this paper, we address this challenge by proposing four methods to reconstruct the initial matrix while maintaining its physical structure. Leveraging advanced techniques, including sequential quadratic programming (SQP), we minimize the impact of noise, ensuring the recovery of the reconstructed matrix. We provide a rigorous proof of convergence for the SQP method, highlighting its effectiveness in achieving reliable reconstructions. Through comparative numerical analyses, we demonstrate the effectiveness of our methods in preserving the original structure of the initial matrix, even in the presence of noise.
In this paper, the optimization problem of orbital transfer strategy for orbital flyby observation missions is studied. A hybrid optimization method is proposed, which is improved to make it more suitable for satellit...
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In this paper, the optimization problem of orbital transfer strategy for orbital flyby observation missions is studied. A hybrid optimization method is proposed, which is improved to make it more suitable for satellite on-board computing. This new algorithm is designed to solve the initial value sensitivity problem of the sequential quadratic programming algorithm (SQP). It is consisted of the depth-first search algorithm (DFS) and the SQP algorithm and thus has the characteristics of fast convergence, high reliability, and good robustness. With this method, the DFS with a large step size is calculated first, and then the optimal value in the calculation result is used as the initial value of the SQP algorithm for further optimization. This method can obtain the approximate optimal solution available in engineering. The numerical simulation of an orbital transfer optimization problem is set to verify the effectiveness of the new hybrid algorithm. The simulation results compared with the genetic algorithm (GA) show that the proposed hybrid algorithm can effectively reduce the on-board resource occupation when getting similar results and thus can meet the needs of satellite on-board computing.
This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum *** structures,commonly encountered in enginee...
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This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum *** structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design *** a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization *** the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical *** comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish ***,particularly novices,pay special attention to their difficulties with a particular ***,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their *** from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening *** addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also *** highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this ***,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.
The current research is a revolution in the field of neural computation as a quite new stochastic technique based on Ricker wavelet neural networks (RWNNs) is developed to analyze the Maxwell fluid (Max-F) boundary la...
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The current research is a revolution in the field of neural computation as a quite new stochastic technique based on Ricker wavelet neural networks (RWNNs) is developed to analyze the Maxwell fluid (Max-F) boundary layer flow (BLF) with heat and mass transfer effects over an elongating surface. The global and local search solvers used with RWNNs are genetic algorithms (GAs) and sequential quadratic programming (SQP) respectively to design a new algorithm i.e. RWNNs-GASQP. The transformed nonlinear system of ODEs is acquired using the physical model represented by the flow and then solved using RWNNs-GASQP solver. The obtained numerical form results are successfully compared with reference results acquired through the Adams technique. The accuracy, convergence and effectiveness of the designed solver are identified using numerous statistical and performance analyses.
The structural safety factor is an essential parameter in aircraft design, representing the ratio of the design load to the operating load. Traditional design methods rely on subjective determination of safety factor ...
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The structural safety factor is an essential parameter in aircraft design, representing the ratio of the design load to the operating load. Traditional design methods rely on subjective determination of safety factor values based on experience, lacking objectivity in quantifying uncertainty. However, with advancements in aircraft design technology and increasing competition in the commercial space market, new-generation hypersonic aircraft with complex load environments require a more optimal approach. Applying a uniform safety factor to each component within subregions of the aircraft leads to overly conservative results and impacts flight performance. To address this limitation, a design scheme that incorporates subregional, differentiated safety factors is necessary. This approach allows for better material utilization and ensures compliance with safety requirements. This paper utilizes reliability-based design optimization theory to consider uncertainty in structural systems. It establishes a mapping relationship between structural reliability and differentiated safety factors, providing safety under uncertainty while guaranteeing weight reduction. Additionally, this paper develops a subregional, differentiated safety factors distribution program to determine the safety factors of different subregions of the structure. Consequently, a refined subregional differentiated safety factors scheme that balances safety and economy is derived.
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