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An Improved Fading Factor-Based Adaptive Robust Filtering algorithm for SINS/GNSS Integration with Dynamic Disturbance Suppression

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REMOTE SENSING 2025年第8期17卷 1449-1449页

作者： Chen, Zhaohao Liu, Yixu Liu, Shangguo Wang, Shengli Yang, Lei Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Natl Deep Sea Base Management Ctr Qingdao 266237 Peoples R China Shandong Univ Sci & Technol Coll Ocean Sci & Engn Qingdao 266590 Peoples R China

Aiming at the problem of nonlinear observation model mismatch and insufficient anti-interference ability of SINS/GNSS integrated navigation system in complex dynamic environment, this paper proposes an adaptive robust filtering algorithm with improved fading factor. Aiming at the problem that the traditional Kalman filter is easy to diverge in severe heave motion and abnormal observation, a multi-source information fusion framework integrating satellite positioning geometric accuracy factor (PDOP), solution quality factor (Q value), effective satellite observation number (Satnum), and residual vector is constructed. The dynamic weight adjustment mechanism is designed to realize the real-time optimization of the fading factor. Through the collaborative optimization of robust estimation theory and adaptive filtering, a dual robust mechanism is constructed by combining the sequential update strategy. In the measurement update stage, the observation weight is dynamically adjusted according to the innovation covariance, and the fading memory factor is introduced in the time update stage to suppress the error accumulation of the model. The experimental results show that compared with EKF, Sage-Husa adaptive filtering and robust filtering algorithms, the three-dimensional positioning accuracy is improved by 47.12%, 35.26%, and 9.58%, respectively, in the vehicle strong maneuvering scene. In the scene of ship-borne heave motion, the corresponding increase is 19.44%, 10.47%, and 8.28%. The research results provide an effective anti-interference solution for navigation systems in high dynamic and complex environments.

关键词： SINS/GNSS integrated navigation robust adaptive filtering kalman filtering optimization algorithm dynamic disturbances positioning accuracy

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High-precision machine learning for predicting latent heat in diverse multicomponent molten salts

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SOLAR ENERGY MATERIALS AND SOLAR CELLS 2025年 281卷

作者： Wang, Xue-meng Tao, Yi-dan Dong, Guan-chen Wang, Shuai-yu Miao, Qi Ding, Hong-liang Lv, Jing Wu, Qiong Jin, Yi Tan, Ling-hua Nanjing Univ Sci & Technol Sch Chem & Chem Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Natl Special Superfine Powder Engn Res Ctr Nanjing 210014 Peoples R China Nanjing Jinhe Energy Mat Co Ltd Nanjing 210047 Jiangsu Peoples R China Nanjing Inst Technol Sch Mat Sci & Engn Jiangsu Key Lab Adv Struct Mat & Applicat Technol Nanjing 211167 Peoples R China

Molten salts in phase change materials offer significant advantages, including high thermal storage density, a wide operational temperature range, and low cost. However, the development of novel high-latent-heat molten salts remains largely empirical. Machine learning offers the potential to expedite theoretical advancements and enable precise, cost-efficient performance predictions. Nonetheless, the diversity of molten salt s complicates the accuracy and generalizability of machine learning models. This study proposes a novel latent heat prediction methodology that integrates data analysis and machine learning. A comprehensive dataset encompassing various inorganic salts was systematically analyzed to extract key features influencing latent heat. Subsequently, a predictive model was constructed by combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO). The PSO-BPNN model demonstrated high predictive accuracy, achieving R2 values of 0.9389 and 0.9413 for binary and ternary molten salts, respectively, with experimental validation indicating prediction errors within 10 %. This approach establishes a high-precision, scalable framework for predicting the latent heat of multicomponent molten salts, thereby advancing the design of salts with tailored thermal properties and offering a valuable reference for predicting other thermophysical characteristics.

关键词： Phase change materials Molten salts Artificial neural network optimization algorithm Latent heat Wide-range prediction

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Meta-optimization for improved efficiency: an integrated methodology

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2375-2386页

作者： Singha, Anjani Kumar Yadav, Gaurav Hasanpuri, Vijay Patel, Rajiv Ranjan Department of Computer Applications SRM Institute of Science and Technology (‘Deemed-to-Be-University’) Delhi-NCR Campus Ghaziabad 201204 India Department of Computer Science Aligarh Muslim University Aligarh India UIET Kurukshetra University Kurukshetra India Haridwar University Roorkee India

The use of optimization algorithms is essential to train neural networks effectively. The usage of a combination of two different optimizers is proposed in this method that, used together, can perform single optimizers in various problems. The objective of this study is to develop a comprehensive framework that combines meta-optimization techniques with traditional optimization methods to enhance performance metrics such as convergence speed, solution quality, and robustness against local minima. The researcher presents ATMO (Adaptive Meta Optimizers), recent deep neural network (DNN) models that are combined with a new optimizer that combines two different optimizers simultaneously. Meta-optimizer considers all of their contributions at once, rather than trying to enhance each one separately. Using a wide range of techniques, DNNs have been the subject of numerous investigations on text and image categorization. Results from these tests demonstrated that the suggested Adaptive Meta Optimizer outperforms individual optimizers. These experiments show that adequate training is achieved by considering a variety of factors such as Keras standard learning rate and convergence, and degradation on the CIFAR-100, CIFAR-10, and MNIST datasets, and the results are obtained using Python. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： AdapTiveMeta Optimizers-ATMO Deep learning Image classification optimization algorithm Optimizers Text classification

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A Bi-fidelity Strategy for optimization under Uncertainty: with Applications to Aircraft Trajectory optimization

A Bi-fidelity Strategy for Optimization under Uncertainty: w...

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AIAA SciTech Forum

作者： Dixon, Thomas O. Gorodetsky, Alex A. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48104 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

We test a simple bi-fidelity strategy for accelerating trajectory optimization under uncertainty in the presence of robust constraints. Our approach combines the accuracy of high-fidelity models with the computational efficiency of low-fidelity counterparts to increase the accuracy of constraint computation for a given cost. Specifically, we accelerate sample average approaches that use high-fidelity models, with additional samples of low-fidelity models for assessing whether or not a risk constraint is violated. The low-fidelity model's purpose is thus to reduce the uncertainty in the estimate of constraint violation, at the expense of introduction of bias. Our empirical test on a simulation of glider dynamics tasked with avoiding a region indicates an order-of-magnitude less failures using the bi-fidelity strategy.

关键词： Optimal Control Problem Sample Average Approximation optimization algorithm Collocation Method Quantiles Runge Kutta Integration Boundary Value Problems Autonomous Systems Space Exploration Python

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Landing optimization on the Special Euclidean Group SE(3) Under Control and State-Triggered Inequality Constraints

Landing Optimization on the Special Euclidean Group SE(3) Un...

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AIAA SciTech Forum

作者： McCann, Brennan S. Nazari, Morad Embry Riddle Aeronaut Univ Aerosp Engn 1 Aerosp Blvd Daytona Beach FL 32114 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper studies rigid body trajectory optimization for spacecraft landing. Formulating the rigid-body dynamics on the special Euclidean group (SE(3)) mitigates singularity and nonuniquness issues of attitude parameterization sets. To perform the optimization problem using these dynamics, classical methods for optimization are generalized to Riemannian manifolds and trajectory optimization problems. Riemannian on-manifold optimization methods are used to provide an algorithm for optimizing the spacecraft landing problem on SE(3). In addition, inequality constraints and control input limits are applied, and the problem is solved using a Riemannian augmented Lagrangian method with trapezoidal collocation.

关键词： Optimal Control Problem Augmented Lagrangian Method optimization algorithm Spacecraft Landing Rigid Body Dynamics Lagrange Multipliers Taylor Series Linear Interpolation Heuristic Approach Search algorithm

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Research on Radar Operation Pattern Recognition Method Based on MLP optimization 24

Research on Radar Operation Pattern Recognition Method Based...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Li, Hao Zhang, Fuqun Feng, Mingyue Zhang, Jiapeng Huang, Linxuan Tian, Tian Air Force Early Warning Acad Wuhan Peoples R China Xidian Univ Xian Peoples R China PLA Unit 93277 Shenyang Peoples R China

ISBN: (纸本)9798400716959

This article delves into an innovative radar working pattern recognition algorithm based on multi-layer perceptron (MLP). Through carefully designed optimization algorithms, we systematically searched and determined the optimal MLP network structure to solve the radar operating pattern recognition problem. In a detailed simulation experiment, we carefully analyzed the effects of various network parameters, including the number of network layers, number of neurons, learning rate, and batch rate. The experimental results show that the MLP network can exhibit optimal performance when the number of layers is 5, the number of neurons is 512, the learning rate is 0.006, and the batch rate is 10. This discovery provides us with a highly promising solution to the problem of radar working pattern recognition.

关键词： Multifunctional radar Radar working mode recognition neural network optimization algorithm multi-layer perceptron

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SlabCutOpt: A code for ornamental stone slab cut optimization

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SOFTWARE IMPACTS 2024年 22卷

作者： Bondua, S. Focaccia, S. Elkarmoty, M. Univ Bologna DICAM Via Terracini 28 I-40131 Bologna Italy Cairo Univ Fac Engn Dept Min Petr & Met Engn 1 Gamaa St Giza 12613 Egypt

Rock masses are naturally affected by discontinuities, joints and fractures that affect their exploitation. After block extraction, different cutting pattern can produce different recovery ratio of the block. The optimization of the cutting pattern can be computed if the discontinuities are mapped by the use of non-destructive methods. We propose a software code able to compute the number of intersected slabs by different cuttings scenarios. The algorithm adopts a brute force computation of several scenarios as specified by the user. The software uses the Open MP library in order to reduce computation time.

关键词： Slabs optimization algorithm Rock Ornamental stone Triangle intersection

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Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 392卷 114616-114616页

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia

This paper presents a novel bio-inspired algorithm inspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks' quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders. (c) 2022 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Metaheuristic algorithm Bio-inspired algorithm Swarm intelligence algorithm Quantum computing Applied mechanics and engineering problems

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Evaluation of the Efficiency of the optimization algorithms for Transfer Learning on the Rice Leaf Disease Dataset

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第10期13卷 83-91页

作者： Quach, Luyl-Da Quoc, Khang Nguyen Quynh, Anh Nguyen Ngoc, Hoang Tran FPT Univ Cantho City Vietnam

To improve the model's efficiency, people use many different methods, including the Transfer Learning algorithm, to improve the efficiency of recognition and classification of image data. The study was carried out to combine optimization algorithms with the Transfer Learning model with MobileNet, MobileNetV2, InceptionV3, Xception, ResNet50V2, DenseNet201 models. Then, testing on rice disease data set with 13.186 images, background removed. The result obtained with high accuracy is the RMSprop algorithm, with an accuracy of 88% when combined with the Xception model, similar to the F1, Xception model, and ResNet50V2 score of 87% when combined with the Adam algorithm. This shows the effect of gradients on the Transition learning model. Research, evaluate and draw the optimal model to build a website to identify diseases on rice leaves, with the main functions including images and recording of disease identification points for better management purposes on diseased areas of rice.

关键词： optimization algorithm transfer learning RMSprop rice leaf disease Adam

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Adjoint optimization of the BGK Equation with an Embedded Neural Network for Reduced-Order Modeling of Hypersonic Flows

Adjoint Optimization of the BGK Equation with an Embedded Ne...

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AIAA SciTech Forum

作者： Ball, Nicholas Daultry Panesi, Marco MacArt, Jonathan F. Sirignano, Justin Univ Oxford Math Inst Oxford England Univ Illinois Aerosp Engn Champaign IL USA Univ Notre Dame Aerosp & Mech Engn Fremantle WA 6959 Australia

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Numerical simulation of the Boltzmann equation, most commonly by the DSMC method, can provide highly accurate descriptions of supersonic, rarefied flows. However, it is computationally expensive in the transitional flow regime, with Knudsen numbers 0.01 less than or similar to Kn less than or similar to 0.1, motivating the use of simpler, but less accurate models such as the BGK equation. We develop and implement optimization methods for calibrating machine learning-based BGK models to high-fidelity DSMC data. We derive and validate an adjoint equation for optimization over the BGK equation with a general form of the collision frequency. We train a neural network model for the collision frequency using automatic differentiation to implement the adjoint equations, and apply this model to the formation of a 1D shock.

关键词： Compressible Flow Neural Networks Reduced Order Modelling Direct Simulation Monte Carlo Direct Numerical Simulation optimization algorithm Conservation of Mass Boltzmann Equation Machine Learning Mach Numbers

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