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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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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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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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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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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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Availability simulation and transfer prediction for bike sharing systems based on discrete event simulation

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SOCIO-ECONOMIC PLANNING SCIENCES 2024年 94卷

作者： Zhou, Yu Chen, Yang Liu, Shenyan Kou, Gang Inner Mongolia Univ Coll Econ & Management Hohhot 010021 Inner Mongolia Peoples R China Sichuan Univ Coll Architecture & Environm Chengdu 610000 Sichuan Peoples R China Xiangjiang Lab Changsha 410205 Hunan Peoples R China

To improve the operational efficiency of Bike Sharing Systems (BSSs), predicting users' real demand plays an essential role. Previous demand predictions have heavily relied on the historical rides (explicit demand) recorded in the BSS management information system. Since there are no bikes available for rent, users' unfulfilled ride expectations (implicit demand) cannot be captured in the system. This paper presents an approach for simulating station-level availability and predicting transfers for BSS. Firstly, the (s, S) inventory strategy combined with an iterative optimization-seeking algorithm to improve the existing simulation model. Then, the K-means method is used to cluster the similarity among stations. The transfer prediction uses source data to aid the learning of target data quickly. Finally, a real case study is conducted to validate the practicability. The simulation experiment indicates that it is possible to estimate the maximum percentage of underestimation of real demand at a station. Through regression analysis, this paper investigates the complex nonlinear relationship between the percentage underestimation of real demand and the easily accessible implicit demand influencing factors. In practical applications, managers can use this method to optimize the number of bikes placed for BSSs.

关键词： Implicit demand Availability simulation Bike sharing system Transfer prediction iterative optimization algorithm

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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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Analysis and optimization of Dynamic and Static Characteristics of the Compliant-Amplifying Mechanisms

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MICROMACHINES 2023年第8期14卷 1502-1502页

作者： Wang, Jin Jing, Zijian Xie, Zongliang Ning, Zongqi Qi, Bo Chinese Acad Sci Key Lab Opt Engn Chengdu 610209 Peoples R China Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Compliant amplifying mechanisms are used widely in high-precision instruments driven by piezoelectric actuators, and the dynamic and static characteristics of these mechanisms are closely related to instrument performance. Although the majority of existing research has focused on analysis of their static characteristics, the dynamic characteristics of the mechanisms affect their response speeds directly. Therefore, this paper proposes a comprehensive theoretical model of compliant-amplifying mechanisms based on the multi-body system transfer matrix method to analyze the dynamic and static characteristics of these mechanisms. The effects of the main amplifying mechanism parameters on the displacement amplification ratio and the resonance frequency are analyzed comprehensively using the control variable method. An iterative optimization algorithm is also used to obtain specific parameters that meet the design requirements. Finally, simulation analyses and experimental verification tests are performed. The results indicate the feasibility of using the proposed theoretical compliant-amplifying mechanism model to describe the mechanism's dynamic and static characteristics, which represents a significant contribution to the design and optimization of compliant-amplifying mechanisms.

关键词： compliant-amplifying mechanisms multi-body system transfer matrix method displacement amplification ratio resonance frequency iterative optimization algorithm

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Matrix-Inverse-Free Deep Unfolding of the Weighted MMSE Beamforming algorithm

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2022年 3卷 65-81页

作者： Pellaco, Lissy Bengtsson, Mats Jalden, Joakim KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Informat Sci & Engn S-10044 Stockholm Sweden

Downlink beamforming is a key technology for cellular networks. However, computing beamformers that maximize the weighted sum rate (WSR) subject to a power constraint is an NP-hard problem. The popular weighted minimum mean square error (WMMSE) algorithm converges to a local optimum but still exhibits considerable complexity. In order to address this trade-off between complexity and performance, we propose to apply deep unfolding to the WMMSE algorithm for a MU-MISO downlink channel. The main idea consists of mapping a fixed number of iterations of the WMMSE into trainable neural network layers. However, the formulation of the WMMSE algorithm, as provided in Shi et al., involves matrix inversions, eigendecompositions, and bisection searches. These operations are hard to implement as standard network layers. Therefore, we present a variant of the WMMSE algorithm i) that circumvents these operations by applying a projected gradient descent and ii) that, as a result, involves only operations that can be efficiently computed in parallel on hardware platforms designed for deep learning. We demonstrate that our variant of the WMMSE algorithm convergences to a stationary point of the WSR maximization problem and we accelerate its convergence by incorporating Nesterov acceleration and a generalization thereof as learnable structures. By means of simulations, we show that the proposed network architecture i) performs on par with the WMMSE algorithm truncated to the same number of iterations, yet at a lower complexity, and ii) generalizes well to changes in the channel distribution.

关键词： Complexity theory Array signal processing Neural networks Downlink Approximation algorithms Network architecture Base stations Deep unfolding downlink beamforming iterative optimization algorithm weighted MMSE algorithm neural network

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A Hybrid Sliding Mode Control Scheme of Markovian Jump Systems via Transition Rates Optimal Design

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第12期51卷 7752-7763页

作者： Cao, Zhiru Niu, Yugang Lam, James Song, Xiaoqi East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China

This article investigates the hybrid sliding mode control problem for the uncertain Markovian jump systems (MJSs) via the transition rates optimal design. The stability condition for the transition rates is first established to ensure the exponential mean-square (EMS) stability of the unforced uncertain MJSs. Then the hybrid design strategy on the sliding mode controller and transition rate matrix is presented to ensure the EMS stability of the controlled system. Moreover, the iterative optimization algorithms are developed to acquire the desirable transition rates, control gain, and decay rate sigma. Finally, some numerical simulation results are provided.

关键词： Hybrid design scheme iterative optimization algorithm Markovian jump systems (MJSs) sliding mode control (SMC) transition rate synthesis

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