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18 pages, 1539 KiB  
Article
A Data-Driven Observer for Wind Farm Power Gain Potential: A Sparse Koopman Operator Approach
by Yue Chen, Bingchen Wang, Kaiyue Zeng, Lifu Ding, Yingming Lin, Ying Chen and Qiuyu Lu
Energies 2025, 18(14), 3751; https://doi.org/10.3390/en18143751 - 15 Jul 2025
Viewed by 148
Abstract
Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are [...] Read more.
Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article
(This article belongs to the Special Issue Modeling, Control and Optimization of Wind Power Systems)
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26 pages, 4555 KiB  
Article
Influence of Geometric Effects on Dynamic Stall in Darrieus-Type Vertical-Axis Wind Turbines for Offshore Renewable Applications
by Qiang Zhang, Weipao Miao, Kaicheng Zhao, Chun Li, Linsen Chang, Minnan Yue and Zifei Xu
J. Mar. Sci. Eng. 2025, 13(7), 1327; https://doi.org/10.3390/jmse13071327 - 11 Jul 2025
Viewed by 175
Abstract
The offshore implementation of vertical-axis wind turbines (VAWTs) presents a promising new paradigm for advancing marine wind energy utilization, owing to their omnidirectional wind acceptance, compact structural design, and potential for lower maintenance costs. However, VAWTs still face major aerodynamic challenges, particularly due [...] Read more.
The offshore implementation of vertical-axis wind turbines (VAWTs) presents a promising new paradigm for advancing marine wind energy utilization, owing to their omnidirectional wind acceptance, compact structural design, and potential for lower maintenance costs. However, VAWTs still face major aerodynamic challenges, particularly due to the pitching motion, where the angle of attack varies cyclically with the blade azimuth. This leads to strong unsteady effects and susceptibility to dynamic stalls, which significantly degrade aerodynamic performance. To address these unresolved issues, this study conducts a comprehensive investigation into the dynamic stall behavior and wake vortex evolution induced by Darrieus-type pitching motion (DPM). Quasi-three-dimensional CFD simulations are performed to explore how variations in blade geometry influence aerodynamic responses under unsteady DPM conditions. To efficiently analyze geometric sensitivity, a surrogate model based on a radial basis function neural network is constructed, enabling fast aerodynamic predictions. Sensitivity analysis identifies the curvature near the maximum thickness and the deflection angle of the trailing edge as the most influential geometric parameters affecting lift and stall behavior, while the blade thickness is shown to strongly impact the moment coefficient. These insights emphasize the pivotal role of blade shape optimization in enhancing aerodynamic performance under inherently unsteady VAWT operating conditions. Full article
(This article belongs to the Special Issue Advanced Studies in Marine Data Analysis)
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22 pages, 3183 KiB  
Article
Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods
by Navid Shirzadi, Dominic Lau and Meli Stylianou
Buildings 2025, 15(13), 2361; https://doi.org/10.3390/buildings15132361 - 5 Jul 2025
Viewed by 371
Abstract
Designing energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random [...] Read more.
Designing energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)—trained on a synthetic dataset of 2000 EnergyPlus-simulated building design scenarios to predict both energy use intensity (EUI) and cost estimates for midrise apartment buildings in the Toronto area. All three models exhibit strong predictive performance, with R2 values exceeding 0.9 for both EUI and cost. XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m2, while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m2. In terms of computational efficiency, the surrogate models significantly outperform a physics-based simulation model, with MLP running approximately 340 times faster and XGBoost and RF achieving over 200 times speedup. This study also examines the effect of training dataset size on model performance, identifying a point of diminishing returns where further increases in data size yield minimal accuracy gains but substantially higher training times. To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. A parametric design configuration analysis further evaluates the models’ sensitivity to changes in building envelope features. Overall, the findings demonstrate that machine learning-based surrogate models can serve as fast, accurate, and interpretable alternatives to traditional simulation methods, supporting efficient decision-making during early-stage building design. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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37 pages, 33539 KiB  
Article
Domain-Separated Quantum Neural Network for Truss Structural Analysis with Mechanics-Informed Constraints
by Hyeonju Ha, Sudeok Shon and Seungjae Lee
Biomimetics 2025, 10(6), 407; https://doi.org/10.3390/biomimetics10060407 - 16 Jun 2025
Viewed by 606
Abstract
This study proposes an index-based quantum neural network (QNN) model, built upon a variational quantum circuit (VQC), as a surrogate framework for the static analysis of truss structures. Unlike coordinate-based models, the proposed QNN uses discrete member and node indices as inputs, and [...] Read more.
This study proposes an index-based quantum neural network (QNN) model, built upon a variational quantum circuit (VQC), as a surrogate framework for the static analysis of truss structures. Unlike coordinate-based models, the proposed QNN uses discrete member and node indices as inputs, and it adopts a separate-domain strategy that partitions the structure for parallel training. This architecture reflects the way nature organizes and optimizes complex systems, thereby enhancing both flexibility and scalability. Independent quantum circuits are assigned to each separate domain, and a mechanics-informed loss function based on the force method is formulated within a Lagrangian dual framework to embed physical constraints directly into the training process. As a result, the model achieves high prediction accuracy and fast convergence, even under complex structural conditions with relatively few parameters. Numerical experiments on 2D and 3D truss structures show that the QNN reduces the number of parameters by up to 64% compared to conventional neural networks, while achieving higher accuracy. Even within the same QNN architecture, the separate-domain approach outperforms the single-domain model with a 6.25% reduction in parameters. The proposed index-based QNN model has demonstrated practical applicability for structural analysis and shows strong potential as a quantum-based numerical analysis tool for future applications in building structure optimization and broader engineering domains. Full article
(This article belongs to the Special Issue Nature-Inspired Metaheuristic Optimization Algorithms 2025)
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14 pages, 4511 KiB  
Article
Development of Surrogate Model for Patient-Specific Lattice-Structured Hip Implant Design via Finite Element Analysis
by Rashwan Alkentar and Tamás Mankovits
Appl. Sci. 2025, 15(7), 3522; https://doi.org/10.3390/app15073522 - 24 Mar 2025
Cited by 2 | Viewed by 766
Abstract
Patient-tailored hip implants are a major area of development in orthopedic surgery. Thanks to the recent developments in titanium printing, the medical industry now places special demands on implants. The lattice design enhances osseointegration and brings the stiffness of the implant closer to [...] Read more.
Patient-tailored hip implants are a major area of development in orthopedic surgery. Thanks to the recent developments in titanium printing, the medical industry now places special demands on implants. The lattice design enhances osseointegration and brings the stiffness of the implant closer to that of the bone, so this is an important direction in the development of hip implant design processes. In our previous research, several lattice structures were compared from a strength perspective, considering surgical specifications regarding cell size. The so-called 3D lattice infill type built into ANSYS with a predefined size has proven to be suitable for medical practice and can be easily manufactured with additive manufacturing techniques. A major step in the implant design process is numerical strength analysis, which elucidates implant material response. Due to the complex geometry of the lattice structure, finite element calculations are extremely time-consuming and require high computation capacity; therefore, the focus of our current research was to develop a surrogate numerical model that provides sufficiently fast and accurate information about the behavior of the designed structure. The developed surrogate model reduces the simulation time by more than one hundred times, and the accuracy of the calculation is more than satisfactory for engineering practice. The deviation from the original model is, on average, below 5%, taking deformation into account. This makes the design phase much more manageable and competitive. Full article
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17 pages, 6954 KiB  
Article
Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges
by Javier Grandío, Brais Barros, Manuel Cabaleiro and Belén Riveiro
Infrastructures 2025, 10(4), 70; https://doi.org/10.3390/infrastructures10040070 - 22 Mar 2025
Viewed by 1016
Abstract
Bridges are essential assets of inland transportation infrastructure; however, they are among the most vulnerable elements of these networks due to deterioration caused by aging and the increasing loads to which they are subjected over time. Consequently, maintenance becomes critical to ensure acceptable [...] Read more.
Bridges are essential assets of inland transportation infrastructure; however, they are among the most vulnerable elements of these networks due to deterioration caused by aging and the increasing loads to which they are subjected over time. Consequently, maintenance becomes critical to ensure acceptable levels of safety and service. Finite element (FE) models are traditionally used to reliably assess structural health, but their computational expense often prevents their extensive use in routine bridge assessments. To overcome this computational limitation, this paper presents an innovative deep learning-based surrogate model for predicting local displacements in bridge structures. By utilizing point cloud data and transformer neural networks, the model provides fast and accurate predictions of displacements, addressing the limitations of traditional methods. A case study of a historical bridge demonstrates the model’s efficiency. The proposed approach integrates spatial data processing techniques, offering a computationally efficient alternative for bridge health monitoring. Our results show that the model achieves mean absolute errors below 0.0213 mm, drastically reducing the time required for structural analysis. Full article
(This article belongs to the Section Infrastructures and Structural Engineering)
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23 pages, 2527 KiB  
Article
Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai and Bo Jiang
Appl. Sci. 2025, 15(6), 2934; https://doi.org/10.3390/app15062934 - 8 Mar 2025
Viewed by 814
Abstract
Resistance is a key index of a ship’s hydrodynamic performance, and studying the design of the bulbous bow is an important method to reduce ship resistance. Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses [...] Read more.
Resistance is a key index of a ship’s hydrodynamic performance, and studying the design of the bulbous bow is an important method to reduce ship resistance. Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses a machine learning method to realize the fast prediction of ship resistance corresponding to different bulbous bows. To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. The coordinates of the control points of the NURBS surface at the bulbous bow are taken as the design variables. Then, a convergence factor is introduced to balance the global and local search abilities of the whale algorithm to improve the convergence speed. The sample space is then iteratively searched using the improved whale algorithm. The results show that the mean absolute error and root mean square error of the CBR model are better than those of the BP and RBF models. The accuracy of the model prediction is significantly improved. The optimized bulbous bow design minimizes the ship resistance, which is reduced by 4.95% compared with the initial ship model. This study provides a reliable and efficient machine learning method for ship resistance prediction. Full article
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19 pages, 4454 KiB  
Article
Reshaping [99mTc]Tc-DT11 to DT14D Tagged with Trivalent Radiometals for NTS1R-Positive Cancer Theranostics
by Panagiotis Kanellopoulos, Berthold A. Nock, Eric P. Krenning and Theodosia Maina
Pharmaceutics 2025, 17(3), 310; https://doi.org/10.3390/pharmaceutics17030310 - 28 Feb 2025
Viewed by 725
Abstract
Background/Objectives: Radiotheranostics of neurotensin subtype 1 receptor (NTS1R)-expressing tumors, like pancreatic, gastrointestinal, or prostate cancer, has attracted considerable attention in recent years. Still, the fast degradation of neurotensin (NT)-based radioligands, by angiotensin-converting enzyme (ACE), neprilysin (NEP), and other proteases, has [...] Read more.
Background/Objectives: Radiotheranostics of neurotensin subtype 1 receptor (NTS1R)-expressing tumors, like pancreatic, gastrointestinal, or prostate cancer, has attracted considerable attention in recent years. Still, the fast degradation of neurotensin (NT)-based radioligands, by angiotensin-converting enzyme (ACE), neprilysin (NEP), and other proteases, has considerably compromised their efficacy. The recently introduced [99mTc]Tc-DT11 (DT11, N4-Lys(MPBA-PEG4)-Arg-Arg-Pro-Tyr-Ile-Leu-OH; N4, 6-(carboxy)-1,4,8,11-tetraazaundecane) has displayed promising uptake in NTS1R-positive tumors in mice and enhanced resistance to both ACE and NEP by virtue of the lateral MPBA-PEG4 (MPBA, 4-(4-methylphenyl)butyric acid; PEG4, 14-amino-3,6,9,12-tetraoxatetradecan-1-oic acid) chain attached to the ε-NH2 of Lys7. We were next interested in investigating whether these qualities could be retained in DT14D, likewise modified at Lys7 but carrying the universal chelator DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) via a (βAla)3 spacer at the α-NH2 of Lys7. This chelator switch enables the labeling of DT14D with a wide range of trivalent radiometals suitable for true theranostic applications, not restricted to the diagnostic imaging of NTS1R-positive lesions only by single-photon emission computed tomography (SPECT). Methods: DT14D was labeled with Ga-67 (a surrogate for the positron emission tomography radionuclide Ga-68), In-111 (for SPECT), and Lu-177 (applied in radiotherapy). The resulting radioligands were tested in NTS1R-expressing pancreatic cancer AsPC-1 cells and mice models. Results: [67Ga]Ga/[111In]In/[177Lu]Lu-DT14D displayed high affinity for human NTS1R and internalization in AsPC-1 cells. They remained >70% intact 5 min after entering the mice’s circulation, displaying NTS1R-specific uptake in AsPC-1 xenografts. Conclusions: Suitably side-chain modified NT analogs show enhanced metabolic stability and hence better prospects for radiotheranostic application in NTS1R-positive cancer. Full article
(This article belongs to the Section Pharmacokinetics and Pharmacodynamics)
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23 pages, 16217 KiB  
Article
Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation
by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng and Wei Gu
Appl. Sci. 2025, 15(4), 1830; https://doi.org/10.3390/app15041830 - 11 Feb 2025
Cited by 2 | Viewed by 1070
Abstract
As the dual carbon goals are being approached, there has been an increase in the number of energy-saving renovation projects for existing buildings. However, building renovation also brings about environmental impacts and incremental costs, which need to be addressed urgently. This study proposes [...] Read more.
As the dual carbon goals are being approached, there has been an increase in the number of energy-saving renovation projects for existing buildings. However, building renovation also brings about environmental impacts and incremental costs, which need to be addressed urgently. This study proposes an integrated artificial intelligence framework to facilitate multi-criteria energy renovation decision making by combining a surrogate-based machine learning (ML) model and an evolutionary generative algorithm to efficiently and accurately identify optimal renovation strategies. To enhance the robustness of the methodology, a comparative analysis of four different ML models—light gradient boosting machine (LightGBM), fast random forest (FRF), multivariate linear regression (MVLR), and artificial neural network (ANN)—was conducted, with LightGBM demonstrating the best performance in terms of accuracy, adaptability, and efficiency. Using the heuristic optimization algorithm and entropy-weighted method, the framework achieved average energy savings of 56.62%, a reduction in carbon emissions of 51.60%, and a 24.27% decrease in life-cycle costs. Compared to local ultra-low-energy building standards, the optimal solutions resulted in a 2.60% reduction in carbon emissions and a 15.85% decrease in life-cycle costs. This integrated framework demonstrates the potential of combining machine learning surrogate models, evolutionary generation, and entropy-weighted methods in building energy retrofitting optimizations, offering a novel, efficient, and adaptable approach for researchers and practitioners seeking to balance energy consumption, carbon emissions, and life-cycle costs in renovation projects. Full article
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30 pages, 9722 KiB  
Article
Hierarchical Online Air Combat Maneuver Decision Making and Control Based on Surrogate-Assisted Differential Evolution Algorithm
by Mulai Tan, Haocheng Sun, Dali Ding, Huan Zhou, Tong Han and Yuequn Luo
Drones 2025, 9(2), 106; https://doi.org/10.3390/drones9020106 - 31 Jan 2025
Viewed by 1106
Abstract
One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization algorithms for solving decision-making problems and the [...] Read more.
One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization algorithms for solving decision-making problems and the mismatch between the planning trajectory and the actual flight trajectory caused by the difference between the decision-making model and the actual aircraft model, this paper proposes a hierarchical on-line air combat maneuvering decision-making and control framework. Considering the real-time constraints, the maneuver decision problem is transformed into an expensive optimization problem at the decision planning layer. The surrogate-assisted differential evolution algorithm is proposed on the basis of the original differential evolution algorithm, and the planning trajectory is obtained through the 5 degrees of freedom (DOF) model. In the control execution layer, the planning trajectory is tracked through the nonlinear dynamic inverse tracking control method to realize the high-precision control of the 6DOF model. The simulation is carried out under four different initial situation scenarios, including head-on neutral, dominant, parallel neutral, and disadvantaged situations. The Monte Carlo simulation results show that the Surrogate-assisted differential evolution algorithm (SADE) can achieve a win rate of over 53% in all four initial scenarios. The proposed maneuver decision and control framework in this article achieves smooth flight trajectories and stable aircraft control, with each decision average taking 0.08 s, effectively solving the real-time problem of intelligent optimization algorithms in maneuver decision problems. Full article
(This article belongs to the Collection Drones for Security and Defense Applications)
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22 pages, 2827 KiB  
Article
Predicting the Dynamic Response of Transmission Tower–Line Systems Under Wind–Rain Loads
by Bo Yang, Yifan Luo, Yingna Li, Lulu Wang and Jiawen Zhang
Electronics 2025, 14(3), 558; https://doi.org/10.3390/electronics14030558 - 30 Jan 2025
Viewed by 729
Abstract
This study, based on existing research on the dynamic response of transmission tower–line systems under wind and rain loads, proposes a method for predicting these responses using the TimesNet deep learning surrogate model. Initially, a numerical model of the tower–line system is developed [...] Read more.
This study, based on existing research on the dynamic response of transmission tower–line systems under wind and rain loads, proposes a method for predicting these responses using the TimesNet deep learning surrogate model. Initially, a numerical model of the tower–line system is developed to generate dynamic response time series data under the influence of wind velocity and rainfall forces. Wind velocity and precipitation intensity are used as inputs for the surrogate model, with the tower’s maximum displacement and the highest tension in the line serving as the corresponding outputs. Afterward, the fast Fourier transform (FFT) is used to transform the original one-dimensional input signals into their corresponding two-dimensional representations. Feature extraction is then performed using an Inception module with 2D convolutional kernels of varying sizes. Finally, based on the amplitude-weighted information obtained through the FFT, the two-dimensional tensors are transformed back into one-dimensional output signals. The experimental results show that the proposed surrogate model provides highly accurate dynamic response predictions, even under complex conditions involving the interaction between transmission towers and lines, as well as the combined effects of wind and rainfall. Full article
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30 pages, 8301 KiB  
Article
A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation
by Xianwei Wu, Liang Hu, Juepeng Zheng, Lanning Wang, Haitian Lu and Haohuan Fu
Remote Sens. 2025, 17(3), 408; https://doi.org/10.3390/rs17030408 - 25 Jan 2025
Viewed by 974
Abstract
The uncertainty of physical parameters is a major factor contributing to poor precipitation simulation performance in Earth system models (ESMs), particularly in tropical and Pacific regions. To address the high computational cost of repetitive ESM runs, this study proposes a multilevel surrogate model-based [...] Read more.
The uncertainty of physical parameters is a major factor contributing to poor precipitation simulation performance in Earth system models (ESMs), particularly in tropical and Pacific regions. To address the high computational cost of repetitive ESM runs, this study proposes a multilevel surrogate model-based parameter optimization framework and applies it to improve the precipitation performance of CAM5. A top-level surrogate model using gradient boosting regression trees (GBRTs) was constructed, leveraging the candidate point (CAND) approach applied to balance exploration and exploitation. A bottom-level surrogate model was then built based on a small, selected dataset; we designed a trust region approach to adjust the sampling region during the bottom-level tuning process. Experimental results demonstrate that the proposed method achieves fast convergence and significantly enhances precipitation simulation accuracy, with an average improvement of 19% in selected regions. In integrating optimization results through a nonuniform parameterization scheme and parameter smoothing, substantial improvements were observed in the South Pacific, Niño, South America, and East Asia. Comparisons with remote sensing data confirm that the optimized precipitation simulations do not introduce significant biases to other variables, validating the effectiveness and robustness of the proposed method. Full article
(This article belongs to the Special Issue Remote Sensing in Environmental Modelling)
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15 pages, 2197 KiB  
Article
Generalized Non-Convex Non-Smooth Group-Sparse Residual Prior for Image Denoising
by Shaohe Wang, Rui Han, Ping Qian and Chen Li
Electronics 2025, 14(2), 353; https://doi.org/10.3390/electronics14020353 - 17 Jan 2025
Viewed by 827
Abstract
Image denoising is a classic yet challenging problem in low-level image processing. Traditional image denoising approaches using convex regularized prior (e.g., L1-norm) often bring bias problems. To address this issue, a novel prior model based on a family of non-convex functions [...] Read more.
Image denoising is a classic yet challenging problem in low-level image processing. Traditional image denoising approaches using convex regularized prior (e.g., L1-norm) often bring bias problems. To address this issue, a novel prior model based on a family of non-convex functions and group sparsity residual (GSC) prior constraint for image denoising is studied. We propose a generalized non-convex GSC prior model for the image denoising problem. We first utilize the group-sparse representation (GSR) before exploiting image prior information. Specifically, to further improve the image denoising performance of the GSC prior model, we employ several typical non-convex surrogate functions for the sparsity constraint. Then, a fast and efficient thresholding algorithm is proposed to minimize the resulting optimization problem. The experimental results have demonstrated that our proposed method can achieve the best reconstruction results compared with other image denoising approaches. Full article
(This article belongs to the Section Electronic Multimedia)
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26 pages, 4650 KiB  
Article
Hydrodeoxygenation of Phenolic Compounds and Lignin Bio-Oil Surrogate Mixture over Ni/BEA Zeolite Catalyst and Investigation of Its Deactivation
by Antigoni G. Margellou, Foteini F. Zormpa, Dimitrios Karfaridis, Stamatia A. Karakoulia and Konstantinos S. Triantafyllidis
Catalysts 2025, 15(1), 48; https://doi.org/10.3390/catal15010048 - 7 Jan 2025
Cited by 2 | Viewed by 1660
Abstract
Lignin is one of the main structural components of lignocellulosic biomass and can be utilized to produce phenolic compounds that can be converted downstream to cycloalkanes and aromatics, which are useful as drop-in road or aviation biofuels. Within this study, the hydrodeoxygenation of [...] Read more.
Lignin is one of the main structural components of lignocellulosic biomass and can be utilized to produce phenolic compounds that can be converted downstream to cycloalkanes and aromatics, which are useful as drop-in road or aviation biofuels. Within this study, the hydrodeoxygenation of model phenolic/aromatic compounds and surrogate mixture simulating the light fraction of lignin fast-pyrolysis bio-oil was performed under mild reaction conditions. Ni/BEA zeolite was selected as a catalyst to investigate the conversion and the product selectivity of alkyl phenols (phenol, catechol, cresols), methoxy-phenols (guaiacol, syringol, creosol), aromatics (anisole, 1,2,3-trimethoxybenzene) and dimer (2-phenoxy-1-phenyl ethanol) compounds towards (alkyl)cycloalkanes. The hydrodeoxygenation of a surrogate mixture of eleven phenolic and aromatic compounds was then studied by investigating the effect of reaction conditions (temperature, time, H2 pressure, surrogate mixture concentration, and catalyst-to-feed ratio). The conversion of model compounds was in the range of 80–100%, towards a 37–81% (alkyl)cycloalkane yield, being strongly dependent on the complexity/side-chain group of the phenolic/aromatic ring. Regarding the hydrodeoxygenation of the surrogate mixture, 59–100% conversion was achieved, with up to a 72% yield of C6–C9 cycloalkanes. Characterization of spent catalysts showed that the hydrodeoxygenation of surrogate mixture led to carbonaceous depositions on the catalyst, which can be limited under lower temperatures and longer reaction conditions, while after regeneration, the physicochemical properties of catalysts can be partially recovered. Full article
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35 pages, 9273 KiB  
Review
A Review of Multi-Fidelity Learning Approaches for Electromagnetic Problems
by Ricardo E. Sendrea, Constantinos L. Zekios and Stavros V. Georgakopoulos
Electronics 2025, 14(1), 89; https://doi.org/10.3390/electronics14010089 - 28 Dec 2024
Cited by 2 | Viewed by 1505
Abstract
The demand for fast and accurate electromagnetic solutions to support current and emerging technologies has fueled the rapid development of various machine learning techniques for applications such as antenna design and optimization, microwave imaging, device diagnostics, and more. Multi-fidelity (MF) surrogate modeling methods [...] Read more.
The demand for fast and accurate electromagnetic solutions to support current and emerging technologies has fueled the rapid development of various machine learning techniques for applications such as antenna design and optimization, microwave imaging, device diagnostics, and more. Multi-fidelity (MF) surrogate modeling methods have shown great promise in significantly reducing computational costs associated with surrogate modeling while maintaining high model accuracy. This work offers a comprehensive review of the available MF surrogate modeling methods in electromagnetics, focusing on specific methodologies, related challenges, and the generation of variable-fidelity datasets. The article is structured around the two main types of electromagnetic problems: forward and inverse. It begins by summarizing key machine learning concepts and limitations. This transitions to discussing multi-fidelity surrogate model architectures and low-fidelity data techniques for the forward problem. Subsequently, the unique challenges of the inverse problem are presented, along with traditional solutions and their limitations. Following this, the review examines MF surrogate modeling approaches tailored to the inverse problem. In conclusion, the review outlines promising future directions in MF modeling for electromagnetics, aiming to provide fundamental insights into understanding these developing methods. Full article
(This article belongs to the Special Issue The Latest Progress in Computational Electromagnetics and Beyond)
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