Prediction of microalgae harvesting efficiency and identification of important parameters for ballasted flotation using an optimized machine learning model

Algal Research, Journal Year: 2025, Volume and Issue: unknown, P. 103985 - 103985

Published: Feb. 1, 2025

Language: Английский

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Prediction of diesel particulate filter regeneration conditions and diesel engine performance under regeneration mode using AMSO-BPNN and combined with XGBoost

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124341 - 124341

Published: Sept. 6, 2024

Language: Английский

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Experimental and numerical evaluation of the influence of voids on sound absorption behaviors of 3D printed continuous flax fiber reinforced PLA composites

Composites Science and Technology, Journal Year: 2024, Volume and Issue: 255, P. 110720 - 110720

Published: June 15, 2024

Language: Английский

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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 119 - 119

Published: Jan. 2, 2025

The continuous and effective monitoring of the water quality small rural rivers is crucial for sustainable development. In this work, machine learning models were established to predict a typical river based on quantity measured data UAV hyperspectral images. Firstly, spectral preprocessed using fractional order derivation (FOD), standard normal variate (SNV), normalization (Norm) enhance response characteristics parameters. Second, method combining Pearson’s correlation coefficient variance inflation factor (PCC–VIF) was utilized decrease dimensionality features improve input data. Again, screened features, back-propagation neural network (BPNN) model optimized mixture genetic algorithm (GA) particle swarm optimization (PSO) as means estimating parameter concentrations. To intuitively evaluate performance hybrid algorithm, its prediction accuracy compared with that conventional algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN PSO–BPNN). results show GA–PSO–BPNN turbidity (TUB), ammonia nitrogen (NH3-N), total (TN), phosphorus (TP) exhibited optimal coefficients determination (R2) 0.770, 0.804, 0.754, 0.808, respectively. Meanwhile, also demonstrated good robustness generalization ability from different periods. addition, we used visualize parameters in study area. This work provides new approach refined rivers.

Language: Английский

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A Copula-ECAC model for estimating aviation noise around airports

Transportation Research Part D Transport and Environment, Journal Year: 2025, Volume and Issue: 142, P. 104666 - 104666

Published: March 3, 2025

Language: Английский

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A novel approach for identifying sweet spots in tight reservoir fracturing engineering based on physical-data dual drive

Journal of Applied Geophysics, Journal Year: 2025, Volume and Issue: unknown, P. 105735 - 105735

Published: April 1, 2025

Language: Английский

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Risk prediction based on oversampling technology and ensemble model optimized by tree-structured parzed estimator

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 111, P. 104753 - 104753

Published: Aug. 12, 2024

Language: Английский

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Prediction of Combustion Characteristic Parameters of High Efficiency and Low Emission Hydrogen-Enriched Compressed Natural Gas Engine Under Closed-Loop Control

SAE technical papers on CD-ROM/SAE technical paper series, Journal Year: 2025, Volume and Issue: 1

Published: Jan. 31, 2025

<div class="section abstract"><div class="htmlview paragraph">Closed-loop combustion control is highly beneficial for improving the efficiency and reducing emissions of spark ignition internal engines. In this paper, key parameter (CA50) closed-loop its effect on were explored experimentally in a six-cylinder hydrogen enriched compressed natural gas (HCNG) engine. Moreover, particle swarm optimization (PSO) back propagation neural network (BPNN) algorithm improved by various hybrid strategies was employed CA50 prediction. The experimental results reveal that has significant impact characteristics HCNG Meanwhile, statistical analysis illustrates follows normal distribution no self-correlation. Considering one-to-one correspondence between timing, it suitable to select as feedback parameter. simulation indicate prediction model established PSO-BPNN method high performance excellent generalization ability, with an average mean absolute error (MAE) 0.25°CA correlation coefficient (<i>R</i>) more than 0.997. To further enhance model’s performance, models optimized compared, concluding can significantly improve convergence speed without sacrificing accuracy. Among them, NaPSO-BPNN fastest speed, CPU running time 73.02% less model.</div></div>

Language: Английский

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Path planning optimization for swine manure‐cleaning robots through enhanced slime mold algorithm with cellular automata

Animal Science Journal, Journal Year: 2024, Volume and Issue: 95(1)

Published: Jan. 1, 2024

One of the primary challenges for robotic manure cleaners in pig farming is to plan shortest path designated cleaning points under specified conditions with minimal processing cost and time, while avoiding collisions. However, pigs are randomly distributed actual farms, which obstructs robots' movement complicates rapid determination optimal solutions. To address these issues, this study introduces concept interaction among cellular automaton cell neighborhoods proposes Cellular Automata Slime Mold Algorithm (CASMA). This enhanced slime mold algorithm accelerates convergence speed improves search accuracy. validate its effectiveness, CASMA was compared four metaheuristic algorithms (ACO, FA, PSO, WPA) through performance tests simulated experiments. Results demonstrate that complex pigsty environments varying numbers pigs, reduces average step consumption by 8.03%, 1.61%, 0.99%, 4.26% saves time averages 13.20%, 20.11%, 10.86%, 6.4%, respectively. In addition, dynamic obstacle experiments, achieved savings 48.27% 56.28% A* TS, respectively, reducing consumption. Overall, enhances efficiency manure-cleaning robots thereby improving animal welfare.

Language: Английский

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Construction and optimization of spatial network structure of waterborne polyurethane modified concrete

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 458, P. 139611 - 139611

Published: Dec. 19, 2024

Language: Английский

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