Suchergebnisse - Bayesian optimization algorithm*
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Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
Veröffentlicht 2025-06-01“… Random Forest emerged as the optimal algorithm, providing robust and accurate UCS predictions. …”This study integrates systematic laboratory testing with advanced machine learning techniques to predict the unconfined compressive strength (UCS) of cement-treated clayey silt from northwestern Iași, Romania. Laboratory experiments generated 185 UCS measurements, examining the effects of cement con...
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Distributed nonlinear model predictive control for building energy systems: An ALADIN implementation study
Veröffentlicht 2025-09-01“… This work presents a comprehensive study of Nonlinear Distributed Model Predictive Control (NDMPC) implementation for building energy systems, comparing Alternating Direction Method of Multipliers (ADMM) and Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) algorithms alongside different modeling approaches. …”The implementation of sophisticated control strategies for building energy systems is crucial for improving energy efficiency and occupant comfort. While nonlinear model predictive control offers promising benefits, its application to large-scale building systems remains challenging due to computati...
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Surface water quality prediction based on BOA-BiLSTM model(基于BOA-BiLSTM模型的地表水水质预测)
Veröffentlicht 2025-05-01“… ∶为准确评估监测条件有限的平原河网小流域河水水质演变趋势，预知水质变化情况，利用浙江省台州市南官河2021年6月至2023年6月的水质监测数据，基于贝叶斯优化算法（Bayesian optimization algorithm，BOA）和双向长短期记忆神经网络（bi-directional long short-term memory，BiLSTM）建立了地表水水质预测模型。 …”∶To accurately assess the water quality evolution trend of small watersheds in plain river networks with limited monitoring conditions and predict the change of water quality in advance, based on the water quality monitoring data of Nanguan River in Taizhou, Zhejiang province from June 2021 to June...
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Physics-Informed Neural Networks: A Review of Methodological Evolution, Theoretical Foundations, and Interdisciplinary Frontiers Toward Next-Generation Scientific Computing
Veröffentlicht 2025-07-01“… The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>72</mn><mo>%</mo></mrow></semantics></math></inline-formula> cost reduction compared to FEM in high-dimensional spaces (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.01</mn><mo>,</mo><mi>n</mi><mo>=</mo><mn>15</mn></mrow></semantics></math></inline-formula> benchmarks). …”Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical break...
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Analysing and Forecasting the Energy Consumption of Healthcare Facilities in the Short and Medium Term. A Case Study
Veröffentlicht 2024-01-01“… Furthermore, all regression algorithms have undergone hyper-parameter optimisation using random search, grid search and Bayesian optimisation to achieve the minimum prediction errors represented by different metrics. …”Healthcare facilities consist of multiple large buildings with complex energy systems and high energy consumption, resulting in high carbon emissions. The increasing trend in energy consumption of these facilities and the process of selecting an energy supplier from the open market requires reliable...
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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network
Veröffentlicht 2025-06-01“… It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gradient boosting algorithms (Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting) and neural network architectures (Deep Neural Network, Convolutional Neural Network). …”This study examines travel mode choice behavior within the context of Thailand’s emerging high-speed rail (HSR) development. It conducts a comparative assessment of predictive capabilities between the conventional Multinomial Logit (MNL) framework and advanced data-driven methodologies, including gr...
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NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE
Veröffentlicht 2021-03-01“… The best results were shown by the feedforward and backpropagation network, architecture with nonlinear autoregressive and learning algorithms: Levenberg-Marquard nonlinear optimization, Bayesian Regularization method and conjugate gradient method. …”The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a model for forecasting the consumption of the p...
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Distribution Ratio Prediction of Major Components in 30%TBP/kerosene-HNO3 System Based on Machine Learning
Veröffentlicht 2025-06-01“… These models were trained based on different datasets, and their hyper-parameters were optimized using algorithms such as grid search, Bayesian optimization, and K-fold cross-validation. …”Spent fuel reprocessing is an important nuclear energy process which aimed at recovering resources and managing radioactive materials to control potential hazards. In this field, Purex technology is widely used for its high efficiency, scalability, and wide applicability. Purex technology, a liquid-...
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3D displacement time series prediction of a north-facing reservoir landslide powered by InSAR and machine learning
Veröffentlicht 2025-07-01“… Utilizing four advanced ML algorithms, i.e. random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and gated recurrent unit (GRU), with Bayesian optimization (BO) for hyperparameter tuning, we applied this innovative approach to the north-facing, slow-moving Xinpu landslide in the Three Gorges Reservoir Area (TGRA) of China. …”Active landslides pose a significant threat globally, endangering lives and property. Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events. Interferometric synthetic aperture radar (InSAR) stands out as an efficient and prevalent...
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Enhancement Ear-based Biometric System Using a Modified AdaBoost Method
Veröffentlicht 2022-12-01“… The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. …”The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accur...
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Fast Nonparametric Inference of Network Backbones for Weighted Graph Sparsification
Veröffentlicht 2025-07-01“… We then construct an efficient and provably optimal greedy algorithm to identify the backbone minimizing our objectives, whose run-time complexity is log-linear in the number of edges. …”Network backbones provide useful sparse representations of weighted networks by keeping only their most important links, permitting a range of computational speedups and simplifying network visualizations. A key limitation of existing network backboning methods is that they either require the specif...
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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease
Veröffentlicht 2020-12-01“… The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. …”Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. The review presents an analysis of literature on the use of ML for diagnos...
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A Federated Learning Model for Detecting Cyberattacks in Internet of Medical Things Networks
Veröffentlicht 2025-01-01“… The XGBoost models are further optimized using a Bayesian method and integrated with an aggregation algorithm to construct an adaptive global model. …”The Internet of Medical Things (IoMT) has significantly enhanced the healthcare sector by enabling advanced connectivity between smart medical devices, improving patient monitoring, and optimizing care quality. However, this increasing connectivity exposes these systems to cyberattacks that can comp...
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Current status and outlook of UWB radar personnel localization for mine rescue
Veröffentlicht 2025-04-01“… Future research directions of UWB radar personnel localization technology for mine rescue operations are proposed: ① optimizing the UWB radar localization system by constructing cross-modal information fusion models and developing highly adaptive signal processing methods to enhance the system's adaptability to post-mining disaster environments; ② improving the applicability of combined static and dynamic target localization by developing hybrid localization algorithms that integrate Bayesian networks or deep belief networks to fuse static and dynamic target features and establishing state-switching-based comprehensive models; ③ improving UWB radar echo processing algorithms, combining adaptive beamforming technology, Multiple Input Multiple Output (MIMO) technology, and optimized K-means++ or entropy-based hierarchical analysis algorithms, effectively distinguishing multi-target position information, and validating their adaptability and reliability in complex environments through extensive simulation experiments. …”Ultra-Wide Band (UWB) radar exhibits strong penetration capability and high resolution, enabling the detection and localization of trapped personnel behind coal-rock collapses in mine disasters. This paper introduces the principles of UWB radar localization and its applications in mine rescue operat...
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Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.
Veröffentlicht 2025-01-01“… Lastly, the Bayesian optimizer algorithm (BOA) is implemented for the hyperparameter selection of the DL technique. …”Intracranial haemorrhage (ICH) is a crucial medical emergency that entails prompt assessment and management. Compared to conventional clinical tests, the need for computerized medical assistance for properly recognizing brain haemorrhage from computer tomography (CT) scans is more mandatory. Various...
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Effects of PET image reconstruction parameters and tumor-to-background uptake ratio on quantification of PET images from PET/MRI and PET/CT systems
Veröffentlicht 2024-09-01“… The most used PET reconstruction algorithm is Ordered Subset Expectation Maximization (OSEM). …”Introduction: PET/CT and PET/MRI are valuable multimodality imaging techniques for visualizing both functional and anatomical information. The most used PET reconstruction algorithm is Ordered Subset Expectation Maximization (OSEM). In OSEM, the image noise increases with increased number of iterati...
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A Multi-Mode Switching Energy Management Strategy Based on Travel Habits for Range-Extended Electric Vehicle
Veröffentlicht 2025-01-01“… The proposed strategy is based on the fuzzy control algorithm, which ensures that the theoretical SoC curve is well followed. …”To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range-extended electric vehicle (REEV), a travel habit-based multi-mode switching energy management strategy (M-MSEMS) has been introduced. REEV has the advantages of high power, high efficiency and long dri...
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Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data
Veröffentlicht 2025-06-01“… We applied five machine learning algorithms—Random Forest, XGBoost, LightGBM, Stacking, and Convolutional Neural Network Transformers (CNNT)—and evaluated their performance using six metrics: R, RMSE, CSI, MAR, FAR, and fbias, on both validation and testing sets. …”Accurate real-time icing grid fields are critical for preventing ice-related disasters during winter and protecting property. These fields are essential for both mapping ice distribution and predicting icing using physical models combined with numerical weather prediction systems. However, developin...
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Estimating comprehensive growth index for drip-irrigated spring maize in junggar basin via satellite imagery and machine learning
Veröffentlicht 2025-09-01“… Based on random forest (RF) and its optimized models (Bayesian-optimized RF and sparrow search algorithm-optimized RF), a CGI estimation model was proposed. …”Crop growth indicators reflect the growth status and productivity potential of crops. The development of remote sensing technology provides a new perspective for modern agricultural crop growth monitoring. This study utilized Sentinel-2 satellite remote sensing data combined with ground-measured gro...
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Predicting Olympic Medal Performance for 2028: Machine Learning Models and the Impact of Host and Coaching Effects
Veröffentlicht 2025-07-01“… The proposed models, through an innovative integration of clustering, dimensionality reduction, and predictive algorithms, provide reliable forecasts and data-driven insights for optimizing national sports strategies. …”This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. The dataset encompasses records of total medals by country, e...
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