Neural Network Prediction Model Research Articles

Monitoring and analysis of settlement and deformation status of high-rise buildings based on nonlinear regression

In order to solve the problems of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, the author proposes a monitoring and analysis of high-rise building settlement deformation status based on nonlinear regression. The author collected and wirelessly transmitted building settlement information through various hardware devices such as sensors and GPRS communication modules. The monitoring data collected by sensors were compared and analyzed to determine the settlement situation of the building. An RBF neural network prediction model was constructed for possible settlement points. Then, the leapfrog algorithm is used to optimize the structural parameters of the RBF neural network. The experimental results show that this method can accurately evaluate the possible settlement of building structures in actual environments, and the prediction error is small, with a maximum relative error of 4.83 %, indicating good warning ability. This method achieved the best actual value fitting curve results, verifying its feasibility in settlement prediction. Subsequently, a more widely applicable settlement detection and prediction system for building complex structures will be established based on the proposed method, in order to promote its large-scale application.

Research on six-joint industrial robotic arm positioning error compensation algorithm based on motion decomposition and improved CIWOA-BP neural network

Motion errors in the trajectory of a six-joint industrial robotic arm’s end-effector can significantly impact machining precision. Complex milling operations can lead to deviations from the intended path due to the robotic arm’s structural characteristics. These errors often exhibit periodic and position-dependent variations, underscoring the need for meticulous control measures. To address this challenge, we propose a novel motion decomposition-based error compensation technique for a six-joint industrial robotic arm. This approach involves breaking down the robot’s motion trajectory into distinct components and constructing prediction models for each component using a BP neural network. These models are then optimized using the Whale Optimization Algorithm (CIWOA) and an adaptive chaotic mapping clustering approach to improve efficiency and global optimization. The proposed method is applied to various motion types of the robotic arm, resulting in substantial enhancements in absolute positioning accuracy. Experimental validation confirms the reliability of the CIWOA-BP neural network prediction model and the effectiveness of the nonparametric accuracy compensation method in refining motion planning precision.

Optimization Study of Steady-State Aerial-Towed Cable Circling Strategy Based on BP Neural Network Prediction

This paper presents models for UAV aerial-towed cables in free-end and fixed-end configurations, crucial for tasks like communication and aerial charging. By establishing a quasi steady-state model, computational results on cable shapes are obtained. To accelerate computations, a backpropagation (BP) neural network prediction model is trained, significantly reducing the computation time. An evaluation function has been developed that integrates both aircraft performance and cable shape considerations to evaluate circling parameters across various states. This function integrates techniques such as BP neural networks and particle swarm optimization (PSO) to refine parameters such as velocities and bank angles for both free-end and fixed-end cables. The results show that the BP neural network accurately predicts cable shapes, achieving a maximum error of 5% in towing force and verticality. Additionally, PSO efficiently optimizes circling parameters, thereby enhancing the effectiveness of the evaluation function in identifying optimal solutions. This approach significantly improves the efficiency of determining optimal circling parameters for UAV aerial-towed cables, thereby contributing to their operational efficacy.

Bidirectional long-short term memory predictor for material removal rate in computer-controlled optical surfacing

In Computer-Controlled Optical Surfacing technology, the precision of the removal function directly affects the accuracy of computer-aided processing software predictions, which in turn influences subsequent polishing machine processing. A key parameter for constructing the removal function is the material removal rate, which is often challenging to obtain accurately. Currently, the Preston equation is widely used to describe the principles of material removal. However, as a linear equation that omits many factors, it struggles to accurately model the removal function in complex machining scenarios. Therefore, this paper proposes a hybrid neural network model combining Convolutional Neural Networks and Bidirectional Long Short-Term Memory to predict the material removal rate. The model's parameters are optimized using an improved Grey Wolf Optimization algorithm, ultimately establishing a removal function closely consistent with an actual removal function. We first tested our method on the PHM2016 Data Challenge dataset, achieving a mean squared error of 6.19 and an R2 of 0.9949, outperforming other mainstream neural network prediction models developed in recent years. Additionally, we further validated the performance of the neural network using a small grinding head polishing dataset, achieving MSE and R2 values of 1.9035 and 0.99902, respectively. Finally, we applied this method to construct the removal function on an actual small grinding head production line. Compared to the traditional Preston equation-based removal function, the predicted residual surface's PV and RMS errors were reduced from 28.24 % to 35.58 %–4.563 % and 4.86 %, respectively. These validation results demonstrate that the proposed method not only facilitates easier acquisition of the removal function model but also significantly enhances the accuracy of computer-aided processing software predictions, thereby better guiding ultra-precision machining processes.

A COMPARATIVE MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN THE END MILLING OF Al 3003 SUBJECTED TO NON-EQUAL CHANNEL ANGULAR PRESSING (NECAP)

This paper highlights the surface roughness optimization of a specific material, Al 3003, which has been subjected to the non-equal channel angular pressing (NECAP) process. Considering spindle speed, feed rate, and depth of cut as input variables and surface roughness as an output variable, experiments have been conducted based on the L27 orthogonal array of the Taguchi method. Four prediction models, namely exponential and response surface methodology (RSM) as mathematical models, and artificial neural networks (ANNs) prediction models with different training algorithms (Bayesian Regularization (BR) and Levenberg–Marquardt (LM)), are proposed. Applying effectiveness and performance criteria, the prediction accuracy of the exponential model (90.35%), RSM (93.07%), BR (97.83%), and LM (97.54%) shows that all proposed prediction models are efficient enough. The ANN model trained with BR is found to be the best fit for predicting surface roughness. In order to optimize surface roughness, a newly introduced optimization method called the Intelligible-in-time Logics Algorithm (ILA) is employed. High spindle speed (1000[Formula: see text]rev/min), low feed rate (100[Formula: see text]mm/min) and depth of cut (0.5[Formula: see text]mm) have been the optimum cutting parameter combinations to obtain minimum surface roughness (0.4956[Formula: see text][Formula: see text]m). The results have been verified by confirmation tests and Particle Swarm Optimization (PSO) method. ILA and PSO predict the same optimum parameter combinations and minimum surface roughness, while ILA performs optimization in less time (114.4[Formula: see text]s), about 3.5 times faster than PSO. The paper’s findings strongly advocate the application of ILA in machining data optimization.

Predicting bridge condition index using an improved back-propagation neural network

The predicted Bridge Condition Index (BCI) is a guiding indicator for formulating preventive maintenance strategies for bridges and is hardly available even if using the most advanced neural network prediction model, owing to the few measured BCIs and neural network model defects. In this paper, an improved Back-Propagation Neural Network (BPNN) model named CPSO-BP-MC is proposed to predict BCI, which has a BPNN as its core, combined with the Coordinated Particle Swarm Optimization (CPSO) algorithm aims to faster convergence and avoid local optimal solution even with less training sample, and the Markov Chain (MC) algorithm aims to further revise the fluctuation of CPSO-BP prediction output due to maintenance and reinforcement. A case study is presented to demonstrate the efficiency of CPSO-BP-MC predicting BCI with fewer training samples. Comparing the predicted BCI of CPSO-BP-MC with the measured BCI and other models, the results show that CPSO-BP-MC converges faster than other models, and can predict the BCI more stably and accurately, and the error between prediction and measured BCI is less than 3 %.

Impact of surface temperature changes on subgrade settlement of permafrost regions railway: a case study of the Qinghai-Tibet railway, China

ABSTRACT Parts of sections in permafrost regions of the Qinghai-Tibet Railway (QTR) have been deformed due to permafrost degradation, the global warming and human activities. Therefore, monitoring the subgrade settlement of the QTR in permafrost regions has been a major working in QTR maintenance. For this reason, this paper investigates the subsidence characteristics and derivative mechanisms of railway subgrades in the permafrost regions of the QTR. Additionally, it analyses the impact mechanisms of surface temperature changes on the subsidence of permafrost subgrades. Building on this investigation, a C-I-GWO-BP neural network prediction model was developed to forecast subgrade settlement in permafrost regions. The findings reveal a substantial correlation (R2 = 0.834) between the deformation of railway subgrades in permafrost regions and changes in surface temperature. Meanwhile，with the intensification of global warming and human activities, the subsidence of permafrost subgrade has shown an increasing trend year by year. The primary areas of deformation are predominantly concentrated in ice-rich permafrost regions, including rivers, lakes, and snowmelt runoff areas. Additionally, when compared to traditional BP neural network models, the C-I-GWO-BP neural network prediction model proposed in this article showcases diminished prediction errors and heightened accuracy in identifying optimal weights and thresholds. This model offers technical support for the efficient, accurate, and automated prediction of railway subgrade subsidence in permafrost regions.

Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network

With the development of the times, the traditional single-factor time series prediction cannot meet the needs of actual prediction, and it is necessary to comprehensively consider the influence of various variables on prediction results. Therefore, we use MATLAB R2022a to predict the multi-factor highway freight volume. According to the relevant data of highway freight volume in Chinese history, the BP neural network prediction model of highway freight volume is established, and the model is coded and calculated in the MATLAB software environment. Through repeated training of the data, the predicted value is finally obtained. The results show that the prediction accuracy of the BP neural network model based on multi-factor prediction is very high. Through the example analysis of China’s highway freight volume, the original data are accurately fitted, and the validity of the highway freight volume prediction model based on BP neural network is proved. Through the prediction of freight volume, the investment in infrastructure construction is improved to promote the development of transportation industry and the progress of social economy.

Local thermal warpage deformation of polypropylene injection molded flat part and neural network prediction model

Local thermal warpage deformation of polypropylene injection molded flat part and neural network prediction model

CGNSDE: Conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation

A new knowledge-based and machine learning hybrid modeling approach, called conditional Gaussian neural stochastic differential equation (CGNSDE), is developed to facilitate modeling complex dynamical systems and implementing analytic formulae of the associated data assimilation (DA). In contrast to the standard neural network predictive models, the CGNSDE is designed to effectively tackle both forward prediction tasks and inverse state estimation problems. The CGNSDE starts by exploiting a systematic causal inference via information theory to build a simple knowledge-based nonlinear model that nevertheless captures as much explainable physics as possible. Then, neural networks are supplemented to the knowledge-based model in a specific way, which not only characterizes the remaining features that are challenging to model with simple forms but also advances the use of analytic formulae to efficiently compute the nonlinear DA solution. These analytic formulae are used as an additional computationally affordable loss to train the neural networks that directly improve the DA accuracy. This DA loss function promotes the CGNSDE to capture the interactions between state variables and thus advances its modeling skills. With the DA loss, the CGNSDE is more capable of estimating extreme events and quantifying the associated uncertainty. Furthermore, crucial physical properties in many complex systems, such as the translate-invariant local dependence of state variables, can significantly simplify the neural network structures and facilitate the CGNSDE to be applied to high-dimensional systems. Numerical experiments based on chaotic systems with intermittency and strong non-Gaussian features indicate that the CGNSDE outperforms knowledge-based regression models, and the DA loss further enhances the modeling skills of the CGNSDE.

Enhancing Spatial Resolution in Tandem Mass Spectrometry Ion/Ion Reaction Imaging Experiments through Image Fusion.

We have recently developed a charge inversion ion/ion reaction to selectively derivatize phosphatidylserine lipids via gas-phase Schiff base formation. This tandem mass spectrometry (MS/MS) workflow enables the separation and detection of isobaric lipids in imaging mass spectrometry, but the images acquired using this workflow are limited to relatively poor spatial resolutions due to the current time and limit of detection requirements for these ion/ion reaction imaging mass spectrometry experiments. This trade-off between chemical specificity and spatial resolution can be overcome by using computational image fusion, which combines complementary information from multiple images. Herein, we demonstrate a proof-of-concept workflow that fuses a low spatial resolution (i.e., 125 μm) ion/ion reaction product ion image with higher spatial resolution (i.e., 25 μm) ion images from a full scan experiment performed using the same tissue section, which results in a predicted ion/ion reaction product ion image with a 5-fold improvement in spatial resolution. Linear regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN) predictive models were tested for this workflow. Linear regression and 2D CNN models proved optimal for predicted ion/ion images of PS 40:6 and SHexCer d38:1, respectively.

Improvised grey wolf optimizer assisted artificial neural network (IGWO-ANN) predictive models to accurately predict the permeate flux of desalination plants

Effective planning, management, and control of industrial plants and processes have exploded in popularity to enhance global sustainability in recent decades. In this arena, computational predictive models have significantly contributed to plant performance optimization. In this regard, this research proposes an Improvised Grey Wolf Optimizer (IGWO) aided Artificial Neural Network (ANN) predictive model (IGWO-ANN Model-1 to 4) to predict the performance (permeate flux) of desalination plants accurately. For this, the proposed models investigated experimental inputs four: salt concentration & feed flow rate, condenser & evaporator inlet temperatures of the plant. Besides, mean squared error (MSE) and the regression coefficients (R2) have been used to assess the models' accuracy. The proposed IGWO-ANN Model-4 shows strong optimization abilities and provides better R2 = 99.3 % with minimum errors (0.004) compared to existing Response Surface Methodology (RSM) (R2 = 98.5 %, error = 0.100), ANN (R2 = 98.8 %, error = 0.060), GWO-ANN (R2 = 98.8 % error = 0.008), models. The proposed models are multitasking, multilayers, and multivariable, capable of accurately analyzing the desalination plant's performance, and suitable for other industrial applications. This study yielded a promising outcome and revealed the significant pathways for the researchers to analyze the desalination plant's performance to save time, money, and energy.

Research on the Influence of Wheel Tread Defects on Rolling Contact Fatigue

With the rapid development of high-speed trains, local rolling contact fatigue caused by special local defect damage forms has become more prominent. This article takes circular defects as the research object, adopts the Jiang Sehitoglu multi-axis fatigue damage criterion based on the critical plane method, establishes a finite element model of wheel tread circular defects, and studies the influence of wheel tread circular defects on wheel rail rolling contact fatigue. Based on finite element analysis to obtain fatigue damage parameters, an improved PSO-BP neural network was used to establish a neural network prediction model for fatigue damage parameters, and the feasibility of the model was demonstrated. The research results show that the main influencing factors on the rolling contact fatigue life of wheel tread defects are shear stress and shear strain; The fatigue damage parameter is maximum at the edge of the wheel tread defect; As the defect distribution moves from the center to both sides, the contact stress and damage parameters gradually decrease; As the depth of the defect increases to the size of the radius, the contact stress and damage parameters first increase and then decrease; As the diameter of the defect increases, the contact stress and damage parameters increase, but the amplitude change gradually tends to stabilize with the increase of the defect diameter. The neural network prediction results indicate that all predicted samples are within a reasonable range, and this neural network model can provide a reference for predicting fatigue damage of wheel tread.

Study on the Cutting Performance and Remaining Life Prediction of Micro-Texture Ball End Milling Cutters for Titanium Alloys

As a fundamental machining tool, the ball end milling cutter plays a crucial role in manufacturing. Due to its low thermal conductivity, the heat generated during the cutting process of titanium alloy materials is not dissipated efficiently, resulting in a substantial cutting heat. This heat leads to chip adhesion and exacerbates the wear of the ball end milling cutter, ultimately affecting its service life. Therefore, studying the residual life of the tool during the cutting process is essential to prevent significant impacts on the product’s surface quality due to tool damage and passivation. Most research on micro-texture cutters is based on experiments that analyze the wear patterns of cutters under various lubrication conditions and their influence on the cutting process. Different neural network prediction models are employed to enhance the accuracy and stability of tool life prediction models. However, the exploration of other superior models for predicting the life of micro-texture cutters remains ongoing. This paper is based on an experiment involving the milling of titanium alloy using a micro-pit-structured ball end milling cutter. It was found that the cutting force of the tool is higher during the initial and later wear stages. During the stable wear stage, the unevenness of the defective layer on the tool surface is reduced, increasing the contact area and reducing the surface pressure, thereby decreasing the cutting force. This study analyzes the influence of micro-pit structural parameters on the wear and milling force of the ball end milling cutter. By evaluating the wear value of the ball end milling cutter after each cut, the wear mechanism of the micro-texture cutter is identified. A deep-learning-based bidirectional long short-term memory (BiLSTM) neural network model for tool life prediction is developed. Through training and validation, the model’s accuracy and stability are continuously improved. A comparative analysis with different predictive models is conducted to determine whether the proposed model offers advantages over existing models, which is crucial for maximizing tool utilization and reducing manufacturing costs.

Integrating selective flocculation techniques for enhanced efficiency in manufacturing processes: A novel approach through artificial neural network modeling

The use of medium and low-grade iron ore is gradually becoming more important due to the depletion of high-grade iron ore reserves and stringent environmental acts/rules. Slimes from iron ore washing were discarded in tailing dams; however, there is currently consideration for recovering iron values from ultra-fines as well. There are enormous fine dumps that are still unutilised. Hence, this study attempt to delve the optimization of iron ore slimes an indeed requirement for manufacturing and design in industries. Leveraging a flocculation process, coupled with the implementation of an Artificial Neural Network (ANN) predictive model, the Kiriburu processing plant serves as the primary source for iron ore slime samples. Chemical analyses of the collected iron samples reveal a composition featuring 58.24 % iron content, 3.47 % Al2O3, 4.72 % SiO2, and 5.18 % LOI (Loss on Ignition). The investigation explores the performance of the flocculation technique under varying pH levels, different pulp densities, and diverse flocculant dosages. Furthermore, the varying parameters selected are pH from 6 to 11, pulp density from 1 % to 15 %, and flocculant dose from 0.03 to 0.27 mg/g. The study's findings showcase a substantial improvement in the Fe grade of iron ore, escalating from 58.24 % to 66.12 %, with an impressive recovery rate of 82.54 % achieved using a flocculant dosage of 0.09 mg/g at pH 10. Additionally, a performance assessment of the selective flocculation method for iron ore slimes is conducted using an ANN predictive model, with recovery as the pivotal parameter. The input parameters for this model encompass pH, pulp density, and flocculant dosages. Employing a three-layer ANN model with a 3–3–1 architecture and utilizing feed-forward back propagation, the study demonstrates a close alignment between predicted values and experimental data, confirming the model's effectiveness for practical manufacturing applications. Information regarding the potential applications of the model's iron ore slime beneficiation efficacy for the manufacturing sector should be considered. This could entail lower waste, more effectiveness, or cost savings. Emphasise any possible ramifications for sustainability or the environment that would make the study pertinent in a larger perspective.

A comparison of soil water infiltration models of moistube irrigation

As a water-saving method, moistube irrigation has been widely used. To ensure the effectiveness of moistube irrigation the development of an infiltration prediction model under moistube irrigation based on the interaction of multiple factors is required. In this paper, soil water infiltration tests with different bulk densities (1.2 g/cm³, 1.3 g/cm³, and 1.4 g/cm³) and textures (loamy sand, sandy loam, and clay loam) under different pressure heads (1 m, 1.5 m, and 2 m) were designed, and the test data were analyzed by gray correlation theory. The pressure head, bulk density, clay content, silt content, sand content, and initial water content were determined as input variables, and the model structure was composed with two parameters of Kostiakov's model as output variables. Then, the genetic algorithm was used to optimize the back propagation neural network and the particle swarm algorithm to optimize the support vector machine. The soil moisture prediction model under moistube irrigation was established, finally the model was compared and analyzed. The results showed that the consistency effect of the two models was good. However, compared with the BP neural network prediction model optimized by genetic algorithm, the particle swarm algorithm optimized the support vector machine based moistube irrigation prediction model had higher accuracy. The results of this experiment can provide theoretical support for the exploration and modelling prediction of soil water infiltration under moistube irrigation.

Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

Research into Prediction Method for Pressure Pulsations in a Centrifugal Pump Based on Variational Mode Decomposition-Particle Swarm Optimization and Hybrid Deep Learning Models.

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition-Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.

Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010–2023)

Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among older adults. However, predictions based on sequential neural network SO studies and the relationship between physical fitness factors and SO are lacking. This study aimed to develop a predictive model for SO in older adults by focusing on physical fitness factors. A comprehensive dataset of older Korean adults participating in national fitness programs was analyzed using sequential neural networks. Appendicular skeletal muscle/body weight was defined as SO using an anthropometric equation. Independent variables included body fat (BF, %), waist circumference, systolic and diastolic blood pressure, and various physical fitness factors. The dependent variable was a binary outcome (possible SO vs normal). We analyzed hyperparameter tuning and stratified K-fold validation to optimize a predictive model. The prevalence of SO was significantly higher in women (13.81%) than in men, highlighting sex-specific differences. The optimized neural network model and Shapley Additive Explanations analysis demonstrated a high validation accuracy of 93.1%, with BF% and absolute grip strength emerging as the most influential predictors of SO. This study presents a highly accurate predictive model for SO in older adults, emphasizing the critical roles of BF% and absolute grip strength. We identified BF, absolute grip strength, and sit-and-reach as key SO predictors. Our findings underscore the sex-specific nature of SO and the importance of physical fitness factors in its prediction.

Research on Advanced Prediction of Major Equipment Fatigue Load Based on Deep Learning

Abstract The fatigue life of complex service structures is predicted in real-time according to dynamic random loads. Real-time monitoring of stress/strain in structure key parts is important for practical engineering. Based on the deep learning theory, this paper proposes a long and short-term memory convolutional neural network (CNN- LSTM) prediction model with attention mechanism by using the actual workload data set of key components in major equipment. Firstly, convolutional neural networks (CNN) is used to extract the high-dimensional abstract representation from the preprocessed data, and attention mechanism is added to give each channel different attention scores. Then, long and short-term memory (LSTM) is used to process the time series data and output the final prediction results. In this study, data points in the future are predicted, and the final predicted results are root mean square error (RMSE) of 33.2626 and mean absolute percentage error (MAPE) of 15.3768, which can well fit the future load change trend. At the same time, the multi-condition identification module is added to the code, which can better adapt to the changing load of mechanical equipment in the actual service process. Finally, this paper also designed an ablation experiment, which proved the effectiveness of the overall structure of the prediction model designed in this study and the necessity of the three parts of CNN, ECA-Net and LSTM. It provides theoretical support and practical engineering application for real-time monitoring and predicting the fatigue life of important equipment.