Propagation Model Research Articles

Short-Term Power Load Forecasting Based on Genetic Algorithm Improved VMD-BP

Initially, the Variational Mode Decomposition (VMD) technique is applied to break down the original load data into multiple Intrinsic Mode Functions (IMFs) along with a residual component. This step effectively tackles the non-stationary and intricate nature of the load data. Next, a Back Propagation (BP) model is utilized to forecast the various segmented components resulting from the decomposition. To further refine the VMD-BP neural network, a genetic algorithm is employed to optimize parameters such as thresholds, initial weights, and smoothing factors. By aggregating the predictions of the individual components, the final load forecast is produced. Experimental results indicate that the R2 value achieved by the GA-VMD-BP model is 31.71% and 1.46% higher than those of the BP model and VMD-BP model, respectively. Furthermore, the GA-VMD-BP model shows a decrease in MAE by 205.91 MW and 48.51 MW, RMSE by 383.06 MW and 51.64 MW, and MAPE by 2.95% and 0.62%, compared to the other two models.

Caputo fractional rumor propagation model with noise interference

Caputo fractional rumor propagation model with noise interference

Heterogeneous network modelling and robustness analysis of production line operation under the influence of design changes

Various design changes are inevitably encountered during the development of a production line. Modelling and robustness analysis of heterogeneous networks of production line parameter correlations is fundamental to ensure the successful implementation of design changes. According to the organisational composition of the production line, a parametric correlation model of the production line is established via multilayer network theory. Using this model, we then proceed with constructing a risk propagation model of production line design changes based on the load-capacity model. This model is capable of characterising the dynamic propagation process of design changes by adjusting load and capacity distribution parameters. The effect of various dimensional parameters of the production line on the robustness of the parameter correlation network is analysed by numerical simulation. Deliberate attack strategies based on out-degree, deliberate attack based on primary charge, and random attack strategies are proposed. The results reveal that deliberate attacks based on the out-degree are more likely to lead to paralysing parameter correlation networks. This paper provides novel insights for production line designers and developers in comprehending dynamic design change propagation. Furthermore, it presents new perspectives for robustness predicting and enhancing production line parameter correlation networks.

Dielectric identification method and system design of coal gangue based on frequency shift characteristics

This paper investigates the impact of variations in excitation frequency variations on the dielectric properties of coal gangue to enhance identification accuracy in challenging underground conditions. A dielectric identification methodology based on frequency shift characteristics is proposed, focusing on coal gangue samples collected from Zhujidong mine in Huainan mining area, for which a dielectric identification system was designed and constructed. This system facilitates the collection of dielectric response signals from coal gangue at frequencies of 0.4 MHz and 0.5 MHz. To analyze these response signals, a Variation Mode Decomposition (VMD) combined with a median noise reduction technique was implemented. The results indicate that this approach yields optimal noise reduction was achieved, improving the signal-to-noise ratio and root mean square error metrics. The effective voltage values and frequency components of the coal gangue at the specified frequencies were extracted, leading to the development of eight identification models. Notably, the support vector machine (SVM) identification model achieved an accuracy of 98.3%, significantly outperforming K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN), and Back Propagation (BP) models. This research presents a novel approach to addressing the identification challenges associated with the separation of coal gangue in underground environments.

A comparison of Gaussian processes and polynomial chaos emulators in the context of haemodynamic pulse-wave propagation modelling.

Computational modelling of the cardiovascular system is a promising future direction for patient-specific healthcare. However, the computational cost of these simulators is a bottleneck for their practical use in clinic for real-time digital twins. Emulation can overcome this, yet an extensive investigation into cardiovascular emulators is warranted. In this study, we emulate two one-dimensional haemodynamics models of the pulmonary circulation and compare two common emulation strategies: Gaussian processes (GPs) and polynomial chaos expansions (PCEs). We start by reducing the parameter space of the models through global sensitivity analysis, and then compare both emulation strategies using a multivariate, time-series output quantity of interest and a reduced representation using principal component analysis. We compare the emulators in both forward emulation on test data, as well as in their ability to infer parameters in the inverse problem. Our results indicate that GPs slightly outperform PCEs consistently across every comparison, and that a similar performance is obtained for the emulators of the time-dependent output and reduced output.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

Spatial–spectral sparse deep learning combined with a freeform lens enables extreme depth-of-field hyperspectral imaging

Traditional hyperspectral imaging (HI) systems are constrained by a limited depth of field (DoF), necessitating refocusing for any out-of-focus objects. This requirement not only slows down the imaging speed but also complicates the system architecture. It is challenging to trade off among speed, resolution, and DoF within an ultra-simple system. While some studies have reported advancements in extending DoF, the improvements remain insufficient. To address this challenge, we propose a novel, to our knowledge, differentiable framework that integrates an extended DoF (E-DoF) wave propagation model and an achromatic hyperspectral reconstructor powered by deep learning. Through rigorous experimental validation, we have demonstrated that the compact HI system is capable of snapshot capturing of high-fidelity images with an exceptional DoF reaching approximately 5 m, marking a significant improvement of over three orders of magnitude. Additionally, the system achieves over 90% spectral accuracy without aberration, nearly doubling the accuracy performance of existing methods. An asymmetric freeform surface design is introduced for diffractive optical elements, enabling dual functionality with design freedom and E-DoF. The sparse prior conditions for spatial texture and spectral features of hyperspectral cubic data are integrated into the reconstruction network, effectively mitigating texture blurring and chromatic aberration. It foresees that the optimal strategy for achromatic E-DoF can be adopted into other optical systems such as polarization imaging and depth measurement.

Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties

Bowel sounds, produced by intestinal peristalsis, are essential for diagnosing gastrointestinal disorders. However, acquiring and analyzing bowel sounds is challenging due to their unpredictable nature and individual variability. Biological tissues can affect bowel sounds during propagation, resulting in varying degrees of signal attenuation between the sound source and the transducer. This study aims to develop a numerical model of bowel sound propagation in the abdominal cavity, focusing on the impact of different biological layers on signal attenuation. Validation of the model demonstrated strong consistency between simulated and actual bowel sound signals, confirming the model’s accuracy and reliability. The model accounted for adipose tissue thickness, ranging from 5 to 20 mm across individuals, while muscle and skin thicknesses remained constant. Results indicated that signal attenuation increases with both the propagation distance and adipose tissue thickness. These findings provide insights into how tissue layers influence bowel sound propagation, offering a theoretical foundation for developing personalized and precise monitoring devices.

Simultaneous ultrasonic inversion for porosity and texture of additively manufactured polycrystalline metal with differential-Mori-Tanaka model

ABSTRACT Traditional ultrasonic testing technologies have difficulty simultaneously achieving non-destructive characterisation of both porosity and texture of additively manufactured metallic polycrystals. Based on an ultrasonic propagation model that comprehensively considers the combined effects of textures and porosity, we propose here an improved regularisation model based on the L1-L2 norm and an improved L-curve method for ultrasonic non-destructive inversion. Using efficient computation strategies based on sensitivity matrix, we achieved simultaneous and high-precision inversion of texture parameter and porosity. Using two selected laser melting Ti-6Al-4 V samples as examples, texture parameters and porosity were measured respectively through electron backscatter diffraction and Archimedes method. These true results are then used for comparing with the ultrasonic non-destructive inversion results of this study. The comparison shows that the mean absolute percentage errors (MAPE) values between the inversion results based on the improved regularisation model and the true values are 6.51% and 6.89% for two AM Ti-6Al-4 V samples respectively, while the MAPE values based on the Tikhonov regularisation model are 13.02% and 8.06%, respectively. These results indicate that the proposed inversion framework is effective and provides a reliable ultrasonic non-destructive testing method for texture parameter and porosity.

A public opinion propagation model for technological disasters.

Public opinion on technological disasters is influenced by unique factors and characteristics. Based on the infectious disease model, this paper develops a public opinion dissemination model for technological disasters, considering factors such as disaster severity, government response, accountability, and the impact of both positive and negative media content. Using differential equation stability theory, we analyze the existence and stability of both the free propagation equilibrium point and the propagation equilibrium point. The next-generation matrix method is applied to calculate the propagation threshold, revealing that disaster severity, government response, and accountability are key factors in the spread of public opinion. Sensitivity analyses examine how these key factors affect public opinion dynamics. A case study on the Shiyan gas explosion in Hubei Province is presented, with microblog data used to calculate model parameters. The proposed public opinion dissemination model is applied to this case and compared with two other models, demonstrating the viability and effectiveness of the developed model. The analyses also show that well-handled government responses can help calm public opinion, even in cases where accountability is lacking. Finally, policy suggestions are offered to enhance public opinion management during technological disasters.

Research on forest fire information propagation model based on IoV

The number of communication base stations in the forest is insufficient, and it is difficult to cover the entire forest. Therefore, when a forest fire occurs, it is difficult for sensors to send the fire data to the monitoring and control center in a timely and complete manner, which will lead to greater disasters. In this paper, the Internet of Vehicles (IoV) communication technologies was employed to ensure the connectivity of communication links in the forest. We adopted 802.11p Enhanced Distributed Channel Access (EDCA) mechanism to represent the Vehicle-to-Vehicle communication performance, and transformed Markov model to Z domain to analyze real-time communication performance. The wireless communication in the forest fire scenario was simulated, and the experimental results show that by using EDCA, fire related emergency safety information will be preferentially sent through the high-priority transmission queue, with the minimum transmission delay less than 0.2 ms and the maximum packet delivery ratio up to 0.995. When the number of vehicles reaches 110 and above, the network throughput can be maintained at 6Mbps/s.

Application of GGNN inference propagation model for martial art intensity evaluation

Abstract Moderate martial art exercise can enhance physical fitness and promote physical and mental health, but excessive exercise intensity may cause damage to the body. To address this issue, this study adopts the forward maximum matching method and a pre-trained conditional random field model to extract semi-structured and unstructured data to construct a knowledge graph for martial arts intensity assessment. A gated graph neural network is utilized to evaluate exercise intensity and to optimize it through the use of parallel computing. The sampling-optimized model exhibited a 0.13 ms reduction in processing time compared to the second-best model, while the model with an optimized training process demonstrated a 6.7 s reduction after 100 rounds of training, outperforming the second-best model. The evaluation accuracy of the strength evaluation model in the three groups was 81.6, 86.6, and 91.6%, respectively. The average evaluation accuracy of the optimized model was 7.2 and 13.4% higher than other models, respectively. The optimized martial art exercise intensity evaluation model has the potential to significantly reduce time consumption, thereby enhancing overall efficiency. By reducing computation time, the model can improve productivity, responsiveness, and user experience. Furthermore, it can improve evaluation efficiency and accuracy, making martial art exercise intensity evaluation more precise. Ultimately, the model can effectively assist individuals in selecting suitable exercises.

The propagation mechanism of elastoplastic hydraulic fracture in deep reservoir

Abstract The oil and gas industry is increasingly focusing on exploring and developing resources in deep earth layers. At high temperatures, confining pressures, and geostress differences, rock has the mechanical characteristics of plastic enhancement, which leads to the unclear mechanism of hydraulic fracture expansion. The current fracturing model and construction design lack pertinence, and the fracturing reform is difficult to achieve the expected effect. This paper established a model of elastoplastic hydraulic fracture propagation in deep reservoirs. It considered the enhancement of plasticity by examining the elastoplastic deformation and nonlinear fracturing characteristics of the rock. The results confirmed that the hydraulic fractures in deep reservoirs propagated due to plastic energy dissipation after fracture tip passivation, while the stress concentration declined, which increased propagation resistance. The relationship between geology, engineering factors, degree of plasticity, and fracture propagation is discussed, while the conditions that promote fracture propagation are analyzed to provide theoretical support for deep reservoir fracturing design.

A 3D ray-tracing based model for radiowave simulations in vegetated environments with wind-induced dynamics

IntroductionThis paper proposes an extension of a ray-tracing based model for radiowave propagation in the presence of vegetation, to account for the wind-induced channel dynamics in vegetated environments. The original propagation model uses various point scatterers with specific re-radiation and it has been proven to be suitable for a wide range of scenarios. However, the swaying motion of the tree branches and leaves, as an effect of the wind, creates a constantly changing environment that influences the propagating radio signals in both amplitude and phase, resulting in signal level fluctuations.MethodsA large measurement campaign intending to record and characterize the effect of wind-induced dynamics in vegetated environments, was conducted in a controlled environment, inside an anechoic chamber. Experiments included various wind directions and at varying speeds: stationary (0 m/s), low (1.9 m/s) and high (4.7 m/s). The dynamic re-radiation pattern of trees present in the radio path were recorded at 20 and 62.4 GHz signal frequencies. The available experimental data was then used to develop a statistical model which is sought to characterize wind-induced dynamics of the point scatterers’ re-radiation function. Finally, the performance of the proposed dynamic model while predicting the received signal level fluctuations inside a tree formation scenario, was assessed against dynamic directional spectra measurements conducted in a controlled environment, for four different artificially generated wind incidences and two wind speeds, low (1.9 m/s) and high (4.7 m/s).Results and discussionThis experiment proved that the proposed elementary model could be an asset on the characterization of the time-varying effects found in vegetation areas under wind influence.

Study on the strength characteristics of sandstone subjected to coupled static and dynamic loads from the perspective of microscopic crack propagation

A series of engineering dynamic disasters, such as rock bursts and roof caving, are closely related to the damage and strength characteristics of rock under dynamic loading. Research on this topic is helpful for accurately understanding the mechanism of rock dynamic disasters, making predictions in advance and taking appropriate measures to reduce losses. To further study the dynamic failure characteristics of rock, a theoretical model of dynamic wing crack propagation in sandstone under the condition of coupled static and dynamic loads was proposed. The effects of the strain rate, prestatic stress and confining pressure on the damage process and strength of sandstone were quantitatively revealed by a theoretical model. The proposed dynamic wing crack propagation model actually represents the tensile damage of rock and is suitable for the macroscopic tensile fracture of rock under the action of a dynamic load. Compared with the SHPB experimental results, the theoretical model can effectively predict the coupled static and dynamic strengths of sandstone and explain the damage and fracture process. In addition, the differences between the theoretical model and the experimental results were analysed in detail. The results of this study can provide a theoretical reference for further understanding the occurrence mechanism of rock mass dynamic disasters and provide a theoretical model basis for future numerical simulation research.

Enhancing noise assessment accuracy: FEM as an advanced complement to CNOSSOS-EU

This study explores the enhancement of noise assessment accuracy by integrating the Finite Element Method (FEM) as a complementary approach to the Common Noise Assessment Methods in Europe (CNOSSOS-EU). CNOSSOS-EU, widely adopted as the reference for strategic noise mapping across Europe and beyond, provides an semi-empirical model for noise propagation based on experimental algorithms and simplifications. While robust, CNOSSOS-EU’s approach can overlook the complex physical and geometric characteristics of certain environments, particularly in detailed or high-stakes noise assessments. To address these limitations, this work demonstrates how FEM—rooted in fundamental physical principles—can serve as a powerful adjunct to CNOSSOS-EU, offering finer detail in modeling noise propagation in complex environments. By comparing and validating FEM-derived results against those of CNOSSOS-EU, this study identifies key discrepancies and analyzes their implications for noise management strategies. The findings underscore the added value of integrating FEM, ultimately supporting more informed decision-making and the development of effective noise control measures.

A thermo-mechanical damage model for thermal crack propagation in brittle materials

A thermo-mechanical damage model for thermal crack propagation in brittle materials

Coupled large deformation phase-field and cohesive zone model for crack propagation in hard-soft multi-materials

Coupled large deformation phase-field and cohesive zone model for crack propagation in hard-soft multi-materials

Hybrid Gaussian mixture model and unscented transformation algorithm for uncertainty propagation within the PUZZLE software

Hybrid Gaussian mixture model and unscented transformation algorithm for uncertainty propagation within the PUZZLE software

Evaluation of a complex fracture network in deep coalbed methane reservoir considering natural fracture tensile and shear failure

Natural fractures develop in deep coalbed methane reservoirs, and the formation mechanism of complex fractures is still unclear. This paper establishes a finite element model of hydraulic fracture propagation in deep coalbed methane reservoirs containing natural fractures in Ordos, China, to simulate and evaluate the complex hydraulic fracture under natural fracture tensile shear failure. Second, the theoretical criterion of hydraulic expansion of deep coalbed methane reservoirs is deduced, and the mechanism of hydraulic fracture expansion is explained. Finally, the relationship between tension/shear failure and hydraulic fracture complexity is analyzed using microseismic data of deep coalbed methane reservoir hydraulic fracturing. The results show that both tensile and shear failure can increase the complexity of hydraulic fractures in deep coalbed methane reservoirs, and shear failure plays a leading role in increasing the complexity of hydraulic fractures. Field microseismic results show that the proportion of shear events positively correlates with the complexity of hydraulic fractures, and the correlation coefficient R2 is 0.9564. Using large injection rate (greater than 20 m3·min−1) is conducive to the shear failure of natural fractures and improves the complexity of hydraulic fractures.

Multi-parametric diffusion spectrum imaging in tuberous sclerosis complex: Identifying cortical tubers and predicting genotypes.

Multi-parametric diffusion spectrum imaging in tuberous sclerosis complex: Identifying cortical tubers and predicting genotypes.