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iterative optimization algorithm to Design Biplanar Coils for Dynamic Magnetoencephalography

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第2期70卷 2085-2094页

作者： Ding, Zhongya Huang, Ziyuan Pang, Maotong Han, Bangcheng Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China

In this article, an iterative optimization algorithm is proposed to design biplanar coils, which is used for dynamic magnetoencephalography to compensate for residual fields in the magnetic shielding room. The effects of magnetic shielding layers and plane's side length on the uniformity are both considered for designing coils. The iterative calculation is used to minimize the side length of the coil plane. The biplanar coils with 1.3-m side length are designed, which consist of three homogeneous-field coils (B-x,B-y and B-z coils and five gradient-field coils dB(x)/dy, dB(x)/dz, dB(y)/dy, dB(y)/dz, rm and dB(z)/dz coils . The coil system can produce homogeneous and gradient fields within 1% error over the volume of 40 cm x 40 cm x 40 cm. Through active magnetic shielding, the central field inside the magnetic room is reduced from 7.56 to 0.17 nT, and standard deviation from the mean value in the target area falls from 1.366 to 0.177 nT. The dynamic auditory stimulation experiment proves that the biplanar coil system will improve the quality of the evoked signals.

关键词： Coils Magnetic shielding Magnetic noise Magnetic resonance imaging Mirrors Superconducting magnets optimization Active magnetic shielding biplanar coils dynamic magnetoencephalography (MEG) iterative optimization algorithm

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iterative optimization algorithm based on electromagnetic model for designing diffractive micro-optical elements

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OPTICS COMMUNICATIONS 2003年第1-3期220卷 7-15页

作者： Feng, D Yan, YB Jin, GF Liu, HT Tan, QF Tsing Hua Univ Dept Precis Instruments State Key Lab Precis Measurement Technol & Instru Beijing 100084 Peoples R China

An iterative optimization algorithm for designing two dimensions, finite aperture, aperiodic diffractive micro-optical elements based on rigorous electromagnetic computation model-the finite-difference time-domain method has been proposed. All aspects relating to the algorithm such as the finite-difference time-domain method, and the optimization process have been discussed in detail. Without any approximation based on scalar theory, the algorithm can present rigorous design results with reasonable computational cost. An aspherical surface lens and four kinds of off-axis lenses for normally incident TE mode have been designed to illustrate our algorithm. Meanwhile, we also consider the influence of quantized-lenses profiles on their diffraction performance. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： diffractive micro-optical elements iterative optimization algorithm electromagnetic computation model angular spectrum propagation method

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Position Error Compensation in Quadrature Analog Magnetic Encoders through an iterative optimization algorithm

Position Error Compensation in Quadrature Analog Magnetic En...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Jorge Lara Ambrish Chandra Ecole de Technologie Superieure University of Quebec

ISBN: (纸本)9781479940318

This paper analyses the position error compensation in quadrature analog magnetic encoders through the iterative linear search optimization algorithm of "steepest descent". In current literature, the sine/cosine signals from the encoder are compensated considering only the gain mismatch, the DC offset components and the non-orthogonality. Nevertheless, the magnetic field distortion due to the non-homogeneity of the magnet and the distortion of the signals due to the mechanical misalignment between the rotation axis of the motor shaft, the magnet center and the chip sensor are generally neglected. In this work, all of these factors are taken into account. The results show that a higher order approximation for modeling the harmonic distortion contained in the encoder signals leads to a slight biasing in the convergence of the compensating parameters. However, the unconstrained and multivariate optimization algorithm of steepest descent fairly minimizes the error from the objective function F(X_n) allowing achieve a compensation efficiency of up to 66%, thus increasing the overall accuracy of this kind of magnetic encoders and improving the performance of any application where they provide rotary position feedback.

关键词： Quadrature analog encoders Position error compensation Harmonic distortion iterative optimization algorithm Optimal convergence

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Robust near-infrared modeling for pharmaceutical powder streams: External variable augmented iterative optimization technology (EVA-IOT)

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EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS 2025年 207卷 114626页

作者： Velez-Silva, Natasha L. Rish, Adam J. Drennen, James K. Anderson, Carl A. Duquesne Univ Grad Sch Pharmaceut Sci Pittsburgh PA 15282 USA Duquesne Univ Duquesne Ctr Pharmaceut Technol Pittsburgh PA 15282 USA

The adoption of pure component models, such as iterative optimization technology (IOT) algorithms, is gaining significant interest in the pharmaceutical industry, primarily because of their calibration-free/minimal calibration requirements for process analytical technology applications. The IOT methods have recently demonstrated great potential for monitoring the quality of continuous powder mixtures by near-infrared (NIR) spectroscopy. However, the dynamic conditions of continuous manufacturing processes may limit the effectiveness of such approaches. Density variations introduced to NIR spectra that are collected from dynamic powder mixtures at different process conditions is detrimental to the drug prediction accuracy and robustness of IOT methods. This work introduces a new method, called external variable augmented iterative optimization technology (EVA-IOT), which incorporates the shape of non-chemical external sources of variability into the pure component spectra matrix to improve the prediction accuracy and robustness of the base IOT algorithm. This approach derives the shape of non-chemical external variables from the latent structure of decomposition methods using NIR spectra acquired from a single mixture at known levels of the external variable. A density-augmented EVA-IOT method was developed and implemented to quantify the active pharmaceutical ingredient (API) in continuous powder mixtures flowing at varying process conditions in a simulated continuous process. The EVA-IOT method demonstrated a significantly enhanced API prediction accuracy and robustness against process variation compared to alternative IOT methods. The overall prediction performance of EVA-IOT was comparable to that of global partial least square (PLS) regression models while reducing the calibration burden up to 97%. This makes EVA-IOT a material-sparing alternative to calibration-intensive robust decomposition modeling approaches for monitoring the quality of continuous pharmaceu

关键词： iterative optimization algorithm Process analytical technology Minimal calibration Model robustness Continuous manufacturing process

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The Pseudoinverse Gradient Descent Method with Eight Branch Directions (8B-PGDM): An Improved Dead Reckoning algorithm Based on the Local Invariance of Navigation

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APPLIED SCIENCES-BASEL 2025年第9期15卷 5049-5049页

作者： Gao, Jialong Liu, Quan Deng, Hanqiang Sun, Lei Huang, Jian Lei, Ming Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

This paper establishes a fundamental connection between the local time invariance of motion parameters and dead reckoning (DR) accuracy. This insight enables the reformulation of navigation parameter estimation as a convex optimization problem solvable through our novel Eight-Branch Pseudoinverse Gradient Descent Method (8B-PGDM). This method addresses non-cooperative positioning challenges in sparse-sensor regimes, particularly enabling real-time trajectory prediction when facing intermittent measurements (e.g., <5 Hz sampling rates) or persistent signal blockages. This method achieves an excellent estimation accuracy with only three samplings and an prediction MSE of 0.7906, significantly better than traditional dead reckoning (DR) methods. This approach effectively mitigates the impact of data scarcity, enabling robust and accurate trajectory predictions in challenging environments.

关键词： dead reckoning algorithm local invariance sparse sensor data iterative optimization algorithm

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Forgetting-Factor Regrets for Online Convex optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第8期69卷 5034-5048页

作者： Liu, Yuhang Zhao, Wenxiao Yin, George Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Connecticut Dept Math Storrs CT 06269 USA

This article develops a class of novel algo-rithms for online convex optimization. The key constructis a forgetting-factor regret. It introduces weights to theobjective functions at each time instanttand allows theweights of the past objective functions decaying to *** establish the forgetting-factor regret bounds of clas-sical algorithms including online gradient descent algo-rithms, online gradient-free algorithms, and online Frank-Wolfe algorithms. In addition, the article introduces onlinegradient descent algorithm with a forgetting factor, andanalyze its performance under the new regret. Sufficientconditions are obtained to guarantee the bounds of theforgetting-factor regret of the above algorithms being of theorder o(1), which guarantees the tracking performance forminimizers of time-varying objective functions. Finally, ourresults are tested through numerical demonstration

关键词： Prediction algorithms optimization Linear programming Heuristic algorithms Convex functions Target tracking Loss measurement Forgetting-factor regret iterative optimization algorithm online convex optimization

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Ultimate pit optimization with environmental problem for open-pit coal mine

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 173卷 366-372页

作者： Xu, Xiaochuan Gu, Xiaowei Wang, Qing Zhao, Yunqi Zhu, Zhenguo Wang, Fengdan Zhang, Zaolin Northeastern Univ Coll Resources & Civil Engn Shenyang Peoples R China Northeastern Univ Sci & Technol Innovat Ctr Smart Water & Resource E Shenyang 110819 Peoples R China UCL Fac Math & Phys Sci London WC1E 6BT England

Open pit mining is an effective way to extract valuable minerals from the earth's crust and generate significant profits for mining companies, however it also causes significant environmental harm to vast tracts of land and the surrounding ecosystems. However, the maximization of economic benefits is still the overall objective of optimizing open pit mine design, while environmental issues have been ignored in attempts to achieve more sustainable mine development. Therefore, estimating ecological costs of mining and considering such costs in the mine design process is an important step towards reducing ecological consequences at the design stage. The aim of the study is to calculate the ecological costs of open-pit coal mining and analyze how these costs affect the ultimate pit delineate. The ecological costs associated with coal mining are calculated according to the carbon emissons from energy consumption and the scope of grassland destroyed by resource extraction. There are three key components of ecological cost that have been identified: ecological service value loss, reclamation cost, and carbon emission cost. Necessary equations are provided for estimating these costs. An iterative optimization algorithm is described for ultimate pit optimization in coal deposits with near-horizontal coal seams. A case study is presented in which the ultimate pits are optimized by both with and without considering ecological costs, and compared to prove the impact of ecological costs on the ultimate pit design.

关键词： Ecological cost Open -pit coal mining Ultimate pit optimization Floating cone exclusion method iterative optimization algorithm

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Probability Calculation and Element optimization of Probabilistic Hesitant Fuzzy Preference Relations Based on Expected Consistency

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2018年第3期26卷 1367-1378页

作者： Zhou, Wei Xu, Zeshui Yunnan Univ Finance & Econ Sch Finance Kunming 650221 Yunnan Peoples R China Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

As a generalized fuzzy number, the hesitant fuzzy element (HFE) has received increasing attention. However, it is noted that the occurring probabilities of the elements in the HFE are equal, which is obviously problematic;thus, the preference relations on the HFEs can be inaccurate. To address this issue, this paper proposes the probabilistic hesitant fuzzy preference relations (PHFPRs) based on the probabilistic HFE (PHFE). It seems difficult to provide accurate probabilities that describe the occurring possibilities of the elements in the PHFPRs. This paper further demonstrates the probability calculation method for the PHFPEs based on a proposed variable, which is called the expected consistency. Moreover, the expected consistency index and the judgment principle are designed to evaluate the degree to which the PHFPRs are consistent. For the inconsistent PHFPRs, this paper presents an iterative optimization algorithm to improve their expected consistency by optimizing some elements in the PHFPRs. When the iteration terminates, the consistent PHFPRs and the priorities of the alternatives are identified. Finally, an example that selects a Ph. D. candidate is presented, and the results demonstrate the feasibility and effectiveness of using the PHFPRs and the consistency methods.

关键词： Element optimization expected consistency iterative optimization algorithm probability calculation probabilistic hesitant fuzzy preference relations (PHFPRs)

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Reconstruction parameters of underground objects based on FDTD and optimization MTHOD

Reconstruction parameters of underground objects based on FD...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Fang, GY China Res Inst Radiowave Prop Shandong 266071 Peoples R China

ISBN: (纸本)0780370317

The inverse of the parameters of underground targets is still highly in expectation in Ground Penetrating Radar(GPR) applications. In this paper, the FDTD method and time-domain optimization algorithm have been used to reconstruct the permittivities of underground multiple targets. An iterative optimization algorithm has been put forward. The forward scattering data are also calculated by 2.5D-FDTD algorithm in order to better simulate the practical GPR situation. The relationship between the convergent rates of the inverse algorithm and the initialization values are studied.

关键词： buried object detection iterative methods radar detection finite difference time-domain analysis optimisation inverse problems permittivity underground targets ground penetrating radar GPR FDTD method time-domain optimization algorithm permittivities underground multiple targets iterative optimization algorithm forward scattering data 2.5D-FDTD algorithm inverse algorithm initialization values

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Sparsity and Adaptivity for Sensor Location optimization of Underwater Conformal Vector Array

Sparsity and Adaptivity for Sensor Location Optimization of ...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Ling, Qing Yang, Chaoran Yu, Yun Liu, Ting Sheng, Xueli PLA Beijing Naval Res Acad Beijing Peoples R China Harbin Engn Univ Minist Ind & Informat Acoust Sci & Technol Lab Harbin Peoples R China Harbin Engn Univ Minist Ind & Informat Key Lab Marine Informat Acquisit & Secur Harbin Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin Peoples R China

ISBN: (纸本)9781728154466

In this paper, a sparse array signal processing algorithm using iterative optimization strategy is proposed to select an optimal sparse-aperiodic subarray in an existing fixed array. The algorithm is based on an Improved Genetic algorithm (IGA) that simultaneously adjusts the inter-senor spacing of a conformal array with arbitrary geometrical configuration. A novel crossover method is developed to improve the convergence performance. The proposed method can achieve lower Peak Sidelobe Level (PSL) using fewer sensors and thereby reduce redundancy. The robustness of the algorithm is also tested using multiple independent simulations.

关键词： iterative optimization algorithm robust sparse array conformal vector array

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