NSTL国家科技图书文献中心

1. Convergence of a stochastic variance reduced Levenberg-Marquardt method NSTL国家科技图书文献中心

Shao, Weiyi | Fan, Jinyan - 《Computational optimization and applications》 - 2025,90(2) - 417～444 - 共28页

摘要：In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | point of the problem* almost surely under certain* | stochastic variance reduced Levenberg-Marquardt method is | proposed for solving it. It is shown that both the

关键词： Least squares problems | Empirical residual optimization problem | Stochastic Levenberg-Marquardt algorithm | Variance reduced method | Mini-batch estimation

2. Development of a design method for burnable poisons in small modular fast reactors NSTL国家科技图书文献中心

Wu Y. | Shen Y.... - 《Annals of nuclear energy》 - 2025,211(Feb.) - 1.1～1.12 - 共12页

摘要： reactors is a complicated problem, the empirical* design* | accelerate calculation, and the optimization* of poison* | . Optimization* reduced core burnup reactivity loss from 10,043* | pcm to 3841 pcm in 3.5 years, and the residual | © 2024 The AuthorsLong-cycle small modular

关键词： Burnable poison | Fast reactor | Genetic algorithm | Monte Carlo | Multi-group

3. A Rolling Bearing Fault Diagnosis Method Based on Improved CEEMDAN and RCMFE NSTL国家科技图书文献中心

Zhiyong Luo | Guangming Zhu ... - 《Journal of circuits, systems and computers》 - 2024,33(1) - 1.1～1.20 - 共20页

摘要：Considering the problem* of residual noise and* | spurious modes in the complete ensemble empirical* mode* | decomposed by empirical* mode decomposition (EMD) to the* | (IMFs). Second, aiming at the uncertainty problem* of* | by fruit fly optimization* algorithm (FOA), and the*

关键词： Rolling bearing fault diagnosis | empirical mode decomposition | multi-scale fuzzy entropy | fruit fly optimization algorithm | probabilistic neural network

4. A support vector regression-based method for modeling geometric errors in CNC machine tools NSTL国家科技图书文献中心

Zhang Chuanjing | Liu Huanlao ... - 《The International Journal of Advanced Manufacturing Technology》 - 2024,131(5/6) - 2691～2705 - 共15页 - 被引量：2

摘要：For the problem* of geometric error prediction* | optimization (IHGWO) algorithm is proposed to optimize the | particle swarm optimization* (PSO) algorithm and dimension* | of the basic grey wolf optimization* (GWO) algorithm* | models using sum squared residual* to quantify geometric*

关键词： Geometric errors | Support vector machine regression | Improved hybrid grey wolf optimization algorithm | CNC machine tools | Error compensation

5. Explainable Hybrid Semi-parametric Model for Prediction of Power Generated by Wind Turbines NSTL国家科技图书文献中心

Alfonso Gijon | Simone Eiraudo ... - 《Computational Science - ICCS 2024: 24th International Conference, Malaga, Spain, July 2-4, 2024, Proceedings, Part V》 - International Conference on Computational Science - 2024, - 299～306 - 共8页

摘要： detection mechanisms. Existing empirical* and physics-based* | submodel to predict the residual* component. This non* | influence of input features on the output of the residual | understanding of the analyzed problem. In the end, this | the potential optimization, of several unmodeled

关键词： Hybrid semi-parametric models | Explainable AI | Wind turbines

6. Learning in Sinusoidal Spaces With Physics-Informed Neural Networks NSTL国家科技图书文献中心

Jian Cheng Wong | Chin Chun Ooi ... - 《IEEE Transactions on Artificial Intelligence》 - 2024,5(3) - 985～1000 - 共16页

摘要： the residual* term from governing partial* | optimization to escape from such a local minimum trap, often | the problem* at hand. A key facet of this article is* | the comprehensive empirical* study that demonstrates* | A physics-informed neural network (PINN) uses

关键词： Mathematical models | Three-dimensional displays | Laplace equations | Artificial intelligence | Poisson equations | Transient analysis | Steady-state

7. Cardiac abnormalities from 12‐Lead ECG signals prediction based on deep convolutional neural network optimized with nomadic people optimization algorithm NSTL国家科技图书文献中心

S. V. Evangelin Soni... | R. Nedunchezhian ... - 《International journal of adaptive control and signal processing》 - 2024,38(4) - 1136～1152 - 共17页 - 被引量：2

摘要： healthcare industries. To overcome this problem, a Deep | Optimization for Cardiac Abnormalities from 12‐Lead ECG | Empirical wavelet transformation (MF‐EEWT) for removing | accuracy of the method. Afterward, Residual* Exemplars* | . Generally, DCNN method does not adopt any optimization

关键词： cardiac abnormalities | deep convolutional neural network | encoder and decoder framework | fully convolutional neural network | nomadic people optimization | visual geometry group network

8. A Microseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA NSTL国家科技图书文献中心

Dijun Rao | Min Huang ... - 《Computer Modeling in Engineering & Sciences》 - 2024,141(1 Pt.1) - 187～217 - 共31页

摘要： called the Black Widow Optimization* Algorithm (BWOA* | WTD to filter out the residual* low- and intermediate* | decomposition performance and solve the problem* of relying on* | significantly better than the commonly used VMD and Empirical | The denoising of microseismic signals is a

关键词： Variational mode decomposition | Microseismic signal | Denoising | Wavelet threshold denoising | Black widow optimization algorithm

9. Research on Residual Surface Stresses and Optimization of Milling Process Parameters NSTL国家科技图书文献中心

Yaohui Zheng | Jingyou Qu - 《International Conference on Mechatronics and Intelligent Control (ICMIC 2023): 21-23 July 2023.Wuhan, China》 - International Conference on Mechatronics and Intelligent Control - 2023, - 1279316.1～1279316.7 - 共7页

摘要： an empirical formula for predicting residual | residual surface compressive stresses. The empirical | parameters on residual* surface stresses during the milling* | residual surface stresses during the milling process, and | optimization was established using optimization theory, and

关键词： 7075 aluminum alloy | residual surface stresses | cutting simulation

10. Alternating Optimization for the Dense Subgraph and Submatrix Problems NSTL国家科技图书文献中心

Bombina, Polina. - Mathematics.;Statist... - Ph.D. - Ames, Brendan - 2023, - 1～143 - 共143页

摘要： in empirical* trials, most theoretical analyses of* | clustering problem. In turn these analyses typically | approaches such as convex optimization. We look at new | heuristics to solve the Dense Submatrix problem* as a model* | problem for clustering. We propose a new convex

摘要：In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | point of the problem* almost surely under certain* | stochastic variance reduced Levenberg-Marquardt method is | proposed for solving it. It is shown that both the

关键词： Least squares problems | Empirical residual optimization problem | Stochastic Levenberg-Marquardt algorithm | Variance reduced method | Mini-batch estimation

摘要： reactors is a complicated problem, the empirical* design* | accelerate calculation, and the optimization* of poison* | . Optimization* reduced core burnup reactivity loss from 10,043* | pcm to 3841 pcm in 3.5 years, and the residual | © 2024 The AuthorsLong-cycle small modular

关键词： Burnable poison | Fast reactor | Genetic algorithm | Monte Carlo | Multi-group

摘要：Considering the problem* of residual noise and* | spurious modes in the complete ensemble empirical* mode* | decomposed by empirical* mode decomposition (EMD) to the* | (IMFs). Second, aiming at the uncertainty problem* of* | by fruit fly optimization* algorithm (FOA), and the*

关键词： Rolling bearing fault diagnosis | empirical mode decomposition | multi-scale fuzzy entropy | fruit fly optimization algorithm | probabilistic neural network

摘要：For the problem* of geometric error prediction* | optimization (IHGWO) algorithm is proposed to optimize the | particle swarm optimization* (PSO) algorithm and dimension* | of the basic grey wolf optimization* (GWO) algorithm* | models using sum squared residual* to quantify geometric*

关键词： Geometric errors | Support vector machine regression | Improved hybrid grey wolf optimization algorithm | CNC machine tools | Error compensation

摘要： detection mechanisms. Existing empirical* and physics-based* | submodel to predict the residual* component. This non* | influence of input features on the output of the residual | understanding of the analyzed problem. In the end, this | the potential optimization, of several unmodeled

关键词： Hybrid semi-parametric models | Explainable AI | Wind turbines

摘要： the residual* term from governing partial* | optimization to escape from such a local minimum trap, often | the problem* at hand. A key facet of this article is* | the comprehensive empirical* study that demonstrates* | A physics-informed neural network (PINN) uses

关键词： Mathematical models | Three-dimensional displays | Laplace equations | Artificial intelligence | Poisson equations | Transient analysis | Steady-state

关键词： cardiac abnormalities | deep convolutional neural network | encoder and decoder framework | fully convolutional neural network | nomadic people optimization | visual geometry group network

摘要： called the Black Widow Optimization* Algorithm (BWOA* | WTD to filter out the residual* low- and intermediate* | decomposition performance and solve the problem* of relying on* | significantly better than the commonly used VMD and Empirical | The denoising of microseismic signals is a

关键词： Variational mode decomposition | Microseismic signal | Denoising | Wavelet threshold denoising | Black widow optimization algorithm

摘要： an empirical formula for predicting residual | residual surface compressive stresses. The empirical | parameters on residual* surface stresses during the milling* | residual surface stresses during the milling process, and | optimization was established using optimization theory, and

关键词： 7075 aluminum alloy | residual surface stresses | cutting simulation

摘要： in empirical* trials, most theoretical analyses of* | clustering problem. In turn these analyses typically | approaches such as convex optimization. We look at new | heuristics to solve the Dense Submatrix problem* as a model* | problem for clustering. We propose a new convex

关键词： Mathematical programming | Algorithms | Machine learning | Heuristics | Convex optimization

4008-161-200 800-990-8900

国家科技图书文献中心

摘要：In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | point of the problem almost surely under certain | stochastic variance reduced Levenberg-Marquardt method is | proposed for solving it. It is shown that both the

关键词： Least squares problems | Empirical residual optimization problem | Stochastic Levenberg-Marquardt algorithm | Variance reduced method | Mini-batch estimation

摘要： reactors is a complicated problem, the empirical design | accelerate calculation, and the optimization of poison | . Optimization reduced core burnup reactivity loss from 10,043 | pcm to 3841 pcm in 3.5 years, and the residual | © 2024 The AuthorsLong-cycle small modular

关键词： Burnable poison | Fast reactor | Genetic algorithm | Monte Carlo | Multi-group

摘要：Considering the problem of residual noise and | spurious modes in the complete ensemble empirical mode | decomposed by empirical mode decomposition (EMD) to the | (IMFs). Second, aiming at the uncertainty problem of | by fruit fly optimization algorithm (FOA), and the

关键词： Rolling bearing fault diagnosis | empirical mode decomposition | multi-scale fuzzy entropy | fruit fly optimization algorithm | probabilistic neural network

摘要：For the problem of geometric error prediction | optimization (IHGWO) algorithm is proposed to optimize the | particle swarm optimization (PSO) algorithm and dimension | of the basic grey wolf optimization (GWO) algorithm | models using sum squared residual to quantify geometric

关键词： Geometric errors | Support vector machine regression | Improved hybrid grey wolf optimization algorithm | CNC machine tools | Error compensation

摘要： detection mechanisms. Existing empirical and physics-based | submodel to predict the residual component. This non | influence of input features on the output of the residual | understanding of the analyzed problem. In the end, this | the potential optimization, of several unmodeled

关键词： Hybrid semi-parametric models | Explainable AI | Wind turbines

摘要： the residual term from governing partial | optimization to escape from such a local minimum trap, often | the problem at hand. A key facet of this article is | the comprehensive empirical study that demonstrates | A physics-informed neural network (PINN) uses

关键词： Mathematical models | Three-dimensional displays | Laplace equations | Artificial intelligence | Poisson equations | Transient analysis | Steady-state

关键词： cardiac abnormalities | deep convolutional neural network | encoder and decoder framework | fully convolutional neural network | nomadic people optimization | visual geometry group network

摘要： called the Black Widow Optimization Algorithm (BWOA | WTD to filter out the residual low- and intermediate | decomposition performance and solve the problem of relying on | significantly better than the commonly used VMD and Empirical | The denoising of microseismic signals is a

关键词： Variational mode decomposition | Microseismic signal | Denoising | Wavelet threshold denoising | Black widow optimization algorithm

摘要： an empirical formula for predicting residual | residual surface compressive stresses. The empirical | parameters on residual surface stresses during the milling | residual surface stresses during the milling process, and | optimization was established using optimization theory, and

关键词： 7075 aluminum alloy | residual surface stresses | cutting simulation

摘要： in empirical trials, most theoretical analyses of | clustering problem. In turn these analyses typically | approaches such as convex optimization. We look at new | heuristics to solve the Dense Submatrix problem as a model | problem for clustering. We propose a new convex

关键词： Mathematical programming | Algorithms | Machine learning | Heuristics | Convex optimization

4008-161-200 800-990-8900

国家科技图书文献中心

摘要：In this paper, we study the empirical residual | optimization problem with a least squares loss function. A | point of the problem* almost surely under certain* | stochastic variance reduced Levenberg-Marquardt method is | proposed for solving it. It is shown that both the

摘要： reactors is a complicated problem, the empirical* design* | accelerate calculation, and the optimization* of poison* | . Optimization* reduced core burnup reactivity loss from 10,043* | pcm to 3841 pcm in 3.5 years, and the residual | © 2024 The AuthorsLong-cycle small modular

摘要：Considering the problem* of residual noise and* | spurious modes in the complete ensemble empirical* mode* | decomposed by empirical* mode decomposition (EMD) to the* | (IMFs). Second, aiming at the uncertainty problem* of* | by fruit fly optimization* algorithm (FOA), and the*

摘要：For the problem* of geometric error prediction* | optimization (IHGWO) algorithm is proposed to optimize the | particle swarm optimization* (PSO) algorithm and dimension* | of the basic grey wolf optimization* (GWO) algorithm* | models using sum squared residual* to quantify geometric*

摘要： detection mechanisms. Existing empirical* and physics-based* | submodel to predict the residual* component. This non* | influence of input features on the output of the residual | understanding of the analyzed problem. In the end, this | the potential optimization, of several unmodeled

摘要： the residual* term from governing partial* | optimization to escape from such a local minimum trap, often | the problem* at hand. A key facet of this article is* | the comprehensive empirical* study that demonstrates* | A physics-informed neural network (PINN) uses

摘要： called the Black Widow Optimization* Algorithm (BWOA* | WTD to filter out the residual* low- and intermediate* | decomposition performance and solve the problem* of relying on* | significantly better than the commonly used VMD and Empirical | The denoising of microseismic signals is a

摘要： an empirical formula for predicting residual | residual surface compressive stresses. The empirical | parameters on residual* surface stresses during the milling* | residual surface stresses during the milling process, and | optimization was established using optimization theory, and

摘要： in empirical* trials, most theoretical analyses of* | clustering problem. In turn these analyses typically | approaches such as convex optimization. We look at new | heuristics to solve the Dense Submatrix problem* as a model* | problem for clustering. We propose a new convex