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The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a critical application of test monitoring data within the field of intelligent geotechnical engineering. This paper introduces an effective combinational prediction algorithm, namely SA-IRMO-BP. By integrating the Improved Radial Movement Optimization (IRMO) algorithm with the simulated annealing (SA) algorithm, we develop a meta-heuristic optimization algorithm (SA-IRMO) to optimize the built-in weights and thresholds of backpropagation neural networks (BPNN). Leveraging this integrated prediction algorithm, we forecast the UBC of soil–cement mixed (SCM) pile composite foundations, yielding the following performance metrics: RMSE = 3.4626, MAE = 2.2712, R = 0.9978, VAF = 99.4339. These metrics substantiate the superior predictive performance of the proposed model. Furthermore, we utilize two distinct datasets to validate the generalizability of the prediction model presented herein, which carries significant implications for the safety and stability of civil engineering projects. [ABSTRACT FROM AUTHOR]

We aimed to improve the detection accuracy of laser methane sensors in expansive temperature application environments. In this paper, a large-scale dataset of the measured concentration of the sensor at different temperatures is established, and a temperature compensation model based on the ISSA-BP neural network is proposed. On the data side, a large-scale dataset of 15,810 sets of laser methane sensors with different temperatures and concentrations was established, and an Improved Isolation Forest algorithm was used to clean the large-scale data and remove the outliers in the dataset. On the modeling framework, a temperature compensation model based on the ISSA-BP neural network is proposed. The quasi-reflective learning, chameleon swarm algorithm, Lévy flight, and artificial rabbits optimization are utilized to improve the initialization of the sparrow population, explorer position, anti-predator position, and position of individual sparrows in each generation, respectively, to improve the global optimization seeking ability of the standard sparrow search algorithm. The ISSA-BP temperature compensation model far outperforms the four models, SVM, RF, BP, and PSO-BP, in model evaluation metrics such as MAE, MAPE, RMSE, and R-square for both the training and test sets. The results show that the algorithm in this paper can significantly improve the detection accuracy of the laser methane sensor under the wide temperature application environment. [ABSTRACT FROM AUTHOR]

The investigation of the ignition delay of hydrocarbon fuel is highly valuable for enhancing combustion efficiency, optimizing fuel thermal efficiency, and mitigating pollutant emissions. This paper has developed a BP-MRPSO neural network model for studying hydrocarbon fuel ignition and clarified the novelty of this model compared to the traditional BP and ANN models from the literature. The model integrates the particle swarm optimization (PSO) algorithm with MapReduce-based parallel processing technology. This integration improves the prediction accuracy and processing efficiency of the model. Compared to the traditional BP model, the BP-MRPSO model can increase the average correlation coefficient, from 0.9745 to 0.9896. The R2 value for predicting fire characteristics using this model can exceed 90%. Meanwhile, when the two hidden layers of both the BP and BP-MRPSO models consist of 9 and 8 neurons, respectively, the accuracy of the BP-MRPSO model is increased by 38.89% compared to the BP model. This proved that the new BP-MRPSO model has the capacity to handle large datasets while achieving great precision and efficiency. The findings could provide a new perspective for examining the properties of fuel ignition, which is expected to contribute to the development and assessment of aviation fuel ignition characteristics in the future. [ABSTRACT FROM AUTHOR]

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop's nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model's prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R2 for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects. [ABSTRACT FROM AUTHOR]

NOx is one of the main sources of pollutants for motor vehicles. Nowadays, many diesel vehicle manufacturers may use emission-cheating equipment to make the vehicles meet compliance standards during emission tests, but the emissions will exceed the standards during actual driving. In order to strengthen the supervision of diesel vehicles for emission monitoring, this article intends to establish a model that can predict the transient emission characteristics of heavy-duty diesel vehicles and provide a solution for remote online monitoring of diesel vehicles. This paper refers to the heavy-duty vehicle National VI emission regulations and uses vehicle-mounted portable emission testing equipment (PEMS) to conduct actual road emission tests on a certain country's VI heavy-duty diesel vehicles. Then, it proposes a new feature engineering processing method that uses gray correlation analysis and principal component analysis to eliminate invalid data and reduce the dimensionality of the aligned data, which facilitates the rapid convergence of the model during the training process. Then, a double-hidden-layer BP (Back propagation) neural network was established, and the improved gray wolf algorithm was used to optimize the threshold and weight of the neural network, and a heavy-duty diesel vehicle NOx emission prediction model was obtained. Through the training of the network, the root mean square error (RMSE) of the improved model on the test set between the predicted value and the true value is 1.9144 (mg/s), and the coefficient of determination (R2) is 0.87024. Compared with single-hidden-layer network and double-hidden-layer BP neural network models, the accuracy of the model has been improved. The model can well predict the actual road NOx emissions of heavy-duty diesel vehicles. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network's initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters. [ABSTRACT FROM AUTHOR]

Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP–PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm's performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP–PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP–PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP–PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results. [ABSTRACT FROM AUTHOR]

Accurate detection of cutting diseases in the process of aeroponic rapid propagation is very important for improving the rooting rate and survival rate of cuttings. This paper proposes to use image processing, with a dataset of the growth of mulberry cuttings and a backward propagation (BP) neural network, to identify mildew on the roots of mulberry branches in the process of rapid propagation, before extracting texture and color features. An intelligent control aeroponics system was designed to control the ambient temperature and humidity of the entire rapid propagation incubator according to the mildew rate, thereby improving the rapid propagation time of aeroponics, as well as the rooting and survival rates. In order to distinguish the extracted features, they were classified and identified using a constructed BP neural network model. The results indicated that the performance of the neutral network showed the lowest mean square error in the validation set after three rounds of training; therefore, the model of the third round was chosen as the best model. Furthermore, the training effect of the model revealed that the BP neural network model had good stability and could accurately identify diseases in the root zone of mulberry cuttings. After using MATLAB for neural network training, the regression results revealed correlation coefficients R of 0.98 for the fitting curve of the training dataset, 0.98 for the fitting curve of the test set, and 0.99 for the fitting curve of the validation set, indicating that the prediction results aligned well with the actual results. It can be concluded that research method described in this paper had excellent performance in identifying the health status of mulberry cuttings during the aeroponics rapid propagation process, and it was able to quickly and accurately identify mulberry cuttings affected by mildew disease with an accuracy rate of 80%. This research provides a technical reference for aeroponics rapid propagation factories and intelligent nurseries. [ABSTRACT FROM AUTHOR]

Higher accuracy in cluster failure prediction can ensure the long-term stable operation of cluster systems and effectively alleviate energy losses caused by system failures. Previous works have mostly employed BP neural networks (BPNNs) to predict system faults, but this approach suffers from reduced prediction accuracy due to the inappropriate initialization of weights and thresholds. To address these issues, this paper proposes an improved arithmetic optimization algorithm (AOA) to optimize the initial weights and thresholds in BPNNs. Specifically, we first introduced an improved AOA via multi-subpopulation and comprehensive learning strategies, called MCLAOA. This approach employed multi-subpopulations to effectively alleviate the poor global exploration performance caused by a single elite, and the comprehensive learning strategy enhanced the exploitation performance via information exchange among individuals. More importantly, a nonlinear strategy with a tangent function was designed to ensure a smooth balance and transition between exploration and exploitation. Secondly, the proposed MCLAOA was utilized to optimize the initial weights and thresholds of BPNNs in cluster fault prediction, which could enhance the accuracy of fault prediction models. Finally, the experimental results for 23 benchmark functions, CEC2020 benchmark problems, and two engineering examples demonstrated that the proposed MCLAOA outperformed other swarm intelligence algorithms. For the 23 benchmark functions, it improved the optimal solutions in 16 functions compared to the basic AOA. The proposed fault prediction model achieved comparable performance to other swarm-intelligence-based BPNN models. Compared to basic BPNNs and AOA-BPNNs, the MCLAOA-BPNN showed improvements of 2.0538 and 0.8762 in terms of mean absolute percentage error, respectively. [ABSTRACT FROM AUTHOR]

Wind power prediction is an important research topic in the wind power industry and many prediction algorithms have recently been studied for the sake of achieving the goal of improving the accuracy of short-term forecasting in an effective way. To tackle the issue of generating a huge transition matrix in the traditional Markov model, this paper introduces a real-time forecasting method that reduces the required calculation time and memory space without compromising the prediction accuracy of the original model. This method is capable of obtaining the state probability interval distribution for the next moment through real-time calculation while preserving the accuracy of the original model. Furthermore, the proposed Markov-based Back Propagation (BP) neural network was optimized using the Particle Swarm Optimization (PSO) algorithm in order to effectively improve the prediction approach with an improved PSO-BP neural network. Compared with traditional methods, the computing time of our improved algorithm increases linearly, instead of growing exponentially. Additionally, the optimized Markov-based PSO-BP neural network produced a better predictive effect. We observed that the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) of the prediction model were 12.7% and 179.26, respectively; compared with the existing methods, this model generates more accurate prediction results. [ABSTRACT FROM AUTHOR]

The prices of different quality river crabs on the market can vary several times. Therefore, the internal quality identification and accurate sorting of crabs are particularly important for improving the economic benefits of the industry. Using existing sorting methods by labor and weight to meet the urgent needs of mechanization and intelligence in the crab breeding industry is difficult. Therefore, this paper proposes an improved BP neural network model based on a genetic algorithm, which can grade the crab quality. We comprehensively considered the four characteristics of crabs as the input variables of the model, namely gender, fatness, weight, and shell color of crabs, among which gender, fatness, and shell color were obtained by image processing technology, whereas weight is obtained using a load cell. First, mature machine vision technology is used to preprocess the images of the crab's abdomen and back, and then feature information is extracted from the images. Next, genetic and backpropagation algorithms are combined to establish a quality grading model for crab, and data training is conducted on the model to obtain the optimal threshold and weight values. Analysis of experimental results reveals that the average classification accuracy reaches 92.7%, which proves that this method can achieve efficient and accurate classification and sorting of crabs, successfully addressing market demand. [ABSTRACT FROM AUTHOR]

Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%. [ABSTRACT FROM AUTHOR]

Long-term monitoring of constructed anti-slide piles can help in understanding the processes by which anti-slide piles are subjected to the thrust of landslides. This paper examined the landslide control project of Badong No. 3 High School. The internal force of an anti-slide pile subjected to long-term action of landslide thrust was studied by Distributed Optical Fiber Sensing (DOFS) technology. The BP neural network was used for model training on the monitored strain values and the calculated bending moment values. The results show the following: (1) The monitoring results of the sensor fibers reflect the actual situation more accurately than steel rebar meters do and can locate the position of the sliding zone more accurately. (2) The bending moments distributed along the anti-slide pile have staged characteristics under the long-term action of landslide thrust. Three stages can be summarized according to the development trend of the bending moment values. These three stages can be divided into two change periods of landslide thrust. (3) The model produced by the BP neural network training can predict the bending moment values. In this paper, the sensing fibers monitoring over a long time interval provides a basis for long-term performance analysis of anti-slide piles and stability evaluation of landslides. Using the BP neural network for training relevant data can provide directions for future engineering monitoring. More novel methods can be devised and utilized that will be both accurate and convenient. [ABSTRACT FROM AUTHOR]

Hydraulic turbines constitute an essential component within the hydroelectric power generation industry, contributing to renewable energy production with minimal environmental pollution. Maintaining stable turbine operation presents a considerable challenge, which necessitates effective fault diagnosis and warning systems. Timely and efficient fault w arnings are particularly vital, as they enable personnel to address emerging issues promptly. Although backpropagation (BP) networks are frequently employed in fault warning systems, they exhibit several limitations, such as susceptibility to local optima. To mitigate this issue, this paper introduces an improved social engineering optimizer (ISEO) method aimed at optimizing BP networks for developing a hydraulic turbine warning system. Experimental results reveal that the ISEO-BP-based approach offers a highly effective fault warning system, as evidenced by superior performance metrics when compared to alternative methods. [ABSTRACT FROM AUTHOR]

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [ABSTRACT FROM AUTHOR]

In order to accurately calculate the geometric characteristics of the twin-screw compressor and obtain the optimal profile parameters, a calculation method for the geometric characteristics of twin-screw compressors was proposed to simplify the profile parameter design in this paper. In this method, the database of geometric characteristics is established by back-propagation (BP) neural network, and the genetic algorithm is used to find the optimal profile design parameters. The effects of training methods and hidden layers on the calculation accuracy of neural network are discussed. The effects of profile parameters, including inner radius of the male rotor, protection angle, radius of the elliptic arc, outer radius of the female rotor on the comprehensive evaluation value composed of length of the contact line, blow hole area and area utilization rate, are analyzed. The results show that the time consumed for the database established by BP neural network is 92.8% shorter than that of the traditional method and the error is within 1.5% of the traditional method. Based on the genetic algorithm, compared with the original profile, the blow hole area of the screw compressor profile optimized by genetic algorithm is reduced by 54.8%, the length of contact line is increased by 1.57% and the area utilization rate is increased by 0.32%. The CFD numerical model is used to verify the optimization method, and it can be observed that the leakage through the blow hole of the optimized model is reduced, which makes the average mass flow rate increase by 5.2%, indicating the effectiveness of the rotor profile parameter optimization method. [ABSTRACT FROM AUTHOR]

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10 − 3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems. [ABSTRACT FROM AUTHOR]

Fast and effective forecasting of the new generation of products is key to enhancing the competitiveness of a company in the market. Although the technological evolution laws in the theory of the solution of inventive problems (TRIZ) have been used to predict the potential states of products for innovation, there is a lack of effective methods to select the best technological evolution law consistently with product replacement and update, and acquiring potentially new technologies and solutions, which relies heavily on designers' experience and makes it impossible for designers to efficiently use the technological evolution laws to stimulate product innovation. Aimed to bridge this gap, this paper proposes an integrated method consisting of three main steps, combining the technological evolution laws with back propagation neural network (BPNN), international patent classification (IPC) knowledge and company's technological distance. The best technical evolution law is first searched by a BPNN. The functional verbs and effects in the IPC are then extracted and searched for potential technologies in the Spyder-integrated development environment. Finally, the company's technological distance is used to select analogous sources of potential solutions in the patent database. The final innovative design is determined based on the ideality. The proposed method is applied in the development of a steel pipe-cutting machine to verify its feasibility. The proposed method reduces the dependence on designers' experience and provides a way to access cross-domain technologies, providing a systematic approach for the technological evolution laws to motivate innovative product design. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we propose a Sine chaos mapping-based improved sparrow search algorithm (SSA) to optimize the BP neural network for trajectory prediction of inland river vessels because of the problems of poor accuracy and easy trapping in local optimum in BP neural networks. Method: First, a standard BP model is constructed based on the AIS data of ships in the Yangtze River section. A Sine-BP model is built using Sine chaos mapping to assign neural network weights and thresholds. Finally, a Sine-SSA-BP model is built using the sparrow search algorithm (SSA) to solve the optimal solutions of the neural network weights and thresholds. Result: The Sine-SSA-BP model effectively improves the initialized population of uniform distribution, and reduces the problem that population intelligence algorithms tend to be premature. Conclusions: The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability than conventional LSTM and SVM, especially in the prediction of corners, which is in good agreement with the real ship navigation trajectory. [ABSTRACT FROM AUTHOR]

The global navigation satellite system (GNSS) and inertial navigation system (INS) integrated navigation system have been widely used in Intelligent Transportation Systems (ITSs). However, the positioning error of integrated navigation systems is rapidly divergent when GNSS outages occur. Motion constraint and back propagation (BP) neural networks can provide additional knowledge to solve this issue. However, the predictions of a neural network have outliers and motion constraint is difficult to adapt according to the motion states of vehicles and boats. Therefore, this paper fused a BP neural network with motion constraints, and proposed a motion-constrained GNSS/INS integrated navigation method based on a BP neural network (MC-BP method). The pseudo-measurement of the GNSS was predicted using a fitting model trained by the BP neural network. At the same time, the prediction outliers were detected and corrected using motion constraint. To assess the performance of the proposed method, simulated and real data experiments were conducted with a vehicle on land and a boat offshore. A classical GNSS/INS integration algorithm, a motion-constrained GNSS/INS algorithm, and the proposed method were compared through data processing. Compared with the classical GNSS/INS integration algorithm and the motion-constrained GNSS/INS algorithm, the positioning accuracies of the proposed method were improved by 90% and 64%, respectively, in the vehicle land experiment. Similar performances were found in the offshore boat experiment. Using the proposed MC-BP method, improved meter-level-positioning results can be achieved with the GNSS/INS integration algorithm when GNSS outages occur. [ABSTRACT FROM AUTHOR]

The analysis of the basic characteristics of various research methods is highly needed to predict the residual life of the pipeline accurately, help managers understand the operational risks, and provide a reference for developing pipeline transportation and maintenance inspection plans and anti-corrosion measures. Based on a comprehensive investigation of the existing research on the residual life of the pipeline, this paper finds that the current mainstream life prediction method, based on historical statistical data, has the shortcomings of inconsistent modeling methods, inconsistent basic data, and a lack of comparative evaluation among methods. Moreover, considering the in-depth study of BP neural network modeling, grey theory modeling, time series modeling, and exponential smoothing modeling, optimal prediction models using different methods based on the same historical data are established. These optimal modeling methods are discussed, and the feasible modeling path for the accurate prediction of the pipeline's residual life is given by comparing the prediction accuracy of each model. In addition, the findings serve as a guide for developing an anti-corrosion strategy by highlighting the contribution of the prediction results of the residual life to pipeline decision-making. By comparison, it is found that the accuracy of the four prediction models is as follows: the grey theory prediction model, the exponential smoothing prediction model, the BP neural network prediction model, and the time series prediction model, from high to low, respectively. [ABSTRACT FROM AUTHOR]

In this paper, eight variables of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and flow are used as network input and slump is used as network output to construct a back-propagation (BP) neural network. On this basis, the learning rate, momentum factor, number of hidden nodes and number of iterations are used as hyperparameters to construct 2-layer and 3-layer neural networks respectively. Finally, the response surface method (RSM) is used to optimize the parameters of the network model obtained previously. The results show that the network model with parameters obtained by the response surface method (RSM) has a better coefficient of determination for the test set than the model before optimization, and the optimized model has higher prediction accuracy. At the same time, the model is used to evaluate the influencing factors of each variable on slump. The results show that flow, water, coarse aggregate and fine aggregate are the four main influencing factors, and the maximum influencing factor of flow is 0.875. This also provides a new idea for quickly and effectively adjusting the parameters of the neural network model to improve the prediction accuracy of concrete slump. [ABSTRACT FROM AUTHOR]

The aero ball joint is pivotal in aircraft duct systems due to its favorable properties, including displacement compensation and flexibility. In the stress assessment of air ducts, ball joints are usually simplified by using "Joints" connections to reduce the convergence problems caused by non-linearity, which requires a high degree of accuracy in the characteristic parameters of the ball joint. Accordingly, this paper builds a high temperature and pressure fatigue test platform to investigate the bending characteristics of the ball joint at different temperatures and pressures and points out the limitations of the current method. Then, a method combining finite element analysis (FEA) and the BP neural network is proposed to obtain the characteristic parameters of the ball joint. The results showed that the bending process of the ball joint tended to have two typically different stiffness properties, which were high rigidity and low rigidity. The bending characteristics were strongly influenced by pressure, but less influenced by temperature. The existing test platform increased the force reaction at the contact areas of the ball joint, resulting in errors in the measurement of characteristic parameters. The BP neural network prediction method could effectively alter the ball joint properties and reduce errors. [ABSTRACT FROM AUTHOR]

Jet fish pumps are efficient hydraulic machinery for fish transportation. Yet, the complex flow phenomenon in it is the major potential risk for damage to fish. The dangerous flow phenomena for fish, such as radial pressure gradient and exposure strain rate, are usually controlled by the structural parameters of jet fish pumps. Therefore, the injury rate of fish can be theoretically decreased by the structural optimization design of jet fish pumps. However, there is a complex nonlinear relation between flow phenomena and key structural parameters. To solve this problem, the present paper established a complex mapping between flow phenomena and structural parameters, based on computational fluid dynamics and a back-propagation neural network. According to this mapping, an NSGA-II multi-objective genetic algorithm was used to optimize the structure of jet fish pumps. The results showed that the optimized jet fish pumps could reduce the internal radial pressure gradient, exposure strain rate and danger zone to 40%, 12.5% and 50% of the pre-optimization level, respectively. Therefore, the optimized jet fish pump could significantly reduce the risk of fish injuries and keep the pump efficiency at a high level. The results could provide a certain reference for relevant structural optimization problems. [ABSTRACT FROM AUTHOR]

The mechanical parameters of surrounding rock are an essential basis for roadway excavation and support design. Aiming at the difficulty in obtaining the mechanical parameters of surrounding rock and large experimental errors, the optimized BP neural network model is proposed in this paper. The mind evolutionary algorithm can adequately search the optimal initial weights and thresholds, while the neural network has the advantage of strong nonlinear prediction ability. So, the optimized BP neural network model (MEA-BP model) takes advantage of the two models. It can not only avoid the local extreme value problem but also improve the accuracy and reliability of the prediction results. Based on the orthogonal test method and finite element analysis method, training samples and test samples are established. The nonlinear relationship between rock mechanical parameters and roadway deformation is established by the BP model and MEA-BP model, respectively. The importance analysis of the three input variables shows that the ∆D is the most important input variable, while ∆BC has the smallest impact. The comparison of prediction performance between the MEA-BP model and BP model demonstrates that the optimized initial weights and thresholds can improve the accuracy of prediction value. Finally, the MEA-BP model has been well applied to predicting the mechanical parameter for the surrounding rock in the Pingdingshan mine area, which proves the accuracy and reliability of the optimized model. [ABSTRACT FROM AUTHOR]

In recent years, big data and artificial intelligence technology have developed rapidly and are now widely used in fields of geophysics, well logging, and well test analysis in the exploration and development of oil and gas. The development of shale gas requires a large number of production wells, so big data and artificial intelligence technology have inherent advantages for evaluating the productivity of gas wells and analyzing the influencing factors for a whole development block. To this end, this paper combines the BP neural network algorithm with random probability analysis to establish a big data method for analyzing the influencing factors on the productivity of shale gas wells, using artificial intelligence and in-depth extraction of relevant information to reduce the unstable results from single-factor statistical analysis and the BP neural network. We have modeled and analyzed our model with a large amount of data. Under standard well conditions, the influences of geological and engineering factors on the productivity of a gas well can be converted to the same scale for comparison. This can more intuitively and quantitatively reflect the influences of different factors on gas well productivity. Taking 100 production wells in the Changning shale gas block as a case, random BP neural network analysis shows that maximum EUR can be obtained when a horizontal shale gas well has a fracture coefficient of 1.6, Type I reservoir of 18 m thick, optimal horizontal section of 1600 m long, and 20 fractured sections. [ABSTRACT FROM AUTHOR]

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal--mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal--mechanical coupling performance of the substation panel under fire. [ABSTRACT FROM AUTHOR]

Surface topography parameters are an important factor affecting the wear resistance of parts, and topography parameters are affected by process parameters in order to explore the influence law of process parameters on surface topography parameters and to find the quantitative relationship between milling surface topography parameters and wear resistance. Firstly, this paper took the surface after high-speed milling as the research object, established the residual height model of the milled surface based on static machining parameters, and analyzed the relationship between the residual height of the surface and the machining parameters. Secondly, a high-speed milling experiment was designed to explore the influence law of processing parameters on surface topography and analyzed the influence law of processing parameters on specific topography parameters; Finally, a friction and wear experiment was designed. Based on the BP neural network, the wear resistance of the milled surface in terms of wear amount and friction coefficient was predicted. Through experimental verification, the maximum error of the prediction model was 16.39%, and the minimum was 6.18%. [ABSTRACT FROM AUTHOR]

The traditional mathematical model of shape memory alloy (SMA) is complicated and difficult to program in numerical analysis. The artificial neural network is a nonlinear modeling method which does not depend on the mathematical model and avoids the inevitable error in the traditional modeling method. In this paper, an optimized neural network prediction model of shape memory alloy and its application for structural vibration control are discussed. The superelastic properties of austenitic SMA wires were tested by experiments. The material property test data were taken as the training samples of the BP neural network, and a prediction model optimized by the genetic algorithm was established. By using the improved genetic algorithm, the position and quantity of the SMA wires were optimized in a three-storey spatial structure, and the dynamic response analysis of the optimal arrangement was carried out. The results show that, compared with the unoptimized neural network prediction model of SMA, the optimized prediction model is in better agreement with the test curve and has higher stability, it can well reflect the effect of loading rate on the superelastic properties of SMA, and is a high precision rate-dependent dynamic prediction model. Moreover, the BP network constitutive model is simple to use and convenient for dynamic simulation analysis of an SMA passive control structure. The controlled structure with optimized SMA wires can inhibit the structural seismic responses more effectively. However, it is not the case that the more SMA wires, the better the shock absorption effect. When SMA wires exceed a certain number, the vibration reduction effect gradually decreases. Therefore, the seismic effect can be reduced economically and effectively only when the number and location of SMA wires are properly configured. When four SMA wires are arranged, the acceptable shock absorption effect is obtained, and the sum of the structural storey drift can be reduced by 44.51%. [ABSTRACT FROM AUTHOR]

In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back propagation (BP) neural network has been applied early in electricity price forecasting, it has the problem of low forecasting accuracy. For this reason, this paper uses the maximum information coefficient and Pearson correlation analysis to determine the main factors affecting electricity price fluctuation as the input factors of the forecasting model. The improved particle swarm optimization algorithm, called simulated annealing particle swarm optimization (SAPSO), is used to optimize the BP neural network to establish the SAPSO-BP short-term electricity price forecasting model and the actual sample data are used to simulate and calculate. The results show that the SAPSO-BP price forecasting model has a high degree of fit and the average relative error and mean square error of the forecasting model are lower than those of the BP network model and PSO-BP model, as well as better than the PSO-BP model in terms of convergence speed and accuracy, which provides an effective method for improving the accuracy of short-term electricity price forecasting. [ABSTRACT FROM AUTHOR]