Search

In order to effectively avoid the problem of imbalance between production and sales of new energy vehicles, the PSO algorithm was used to optimize the parameter iteration process of the BP neural network, to make up for the shortcomings of optimizing the original BP neural network which is easy to fall into the local optimum and slow convergence, a new energy vehicle sales prediction model based on the PSO-BP neural network was structructed, Taking BYD as an example the prediction of the exponential smoothing method, the BP and PSO-BP neural network was carried out. The prediction of BYD, BP and PSO-BP neural network were carried out. The results show that the BP neural network model has a significant advantage over the exponential smoothing model in terms of MSE, MAE and MAPE indicators, and the BP neural network model optimized by the particle swarm algorithm shows a decrease of nearly 7 × 107 in MSE, 3 346 in MAE and 1. 71% in MAPE. It can be seen that the new energy vehicle sales prediction model based on PSO-BP neural network is better than the exponential smoothing model and BP neural network model, particle swarm optimized BP neural network can make the model jump out of the local optimum, accelerate the convergence speed, the prediction results of the error rate is lower, the accuracy is higher, and it has a guiding role in the planning and production of enterprises. [ABSTRACT FROM AUTHOR]

Single-well productivity is a crucial metric for assessing the effectiveness of petroleum reservoir development. The accurate prediction of productivity is essential for achieving the efficient and economical development of oil–gas reservoirs. Traditional productivity prediction methods (empirical formulae and numerical simulation) are limited to specific reservoir types. There are few influencing factors, and a large number of ideal assumptions are made for the assumed conditions when predicting productivity. The application scenario is ideal. However, in tight oil reservoirs, numerous factors affect productivity, and their interactions exhibit significant complexity. Continuing to use traditional reservoir productivity prediction methods may result in significant calculation errors and lead to economic losses in oilfield development. To enhance the accuracy of tight reservoir productivity predictions and achieve economical and efficient development, this paper investigates the tight reservoir in the WZ block of the Beibuwan area, considering the impact of geological and engineering factors on productivity; the random forest tree and Spearman correlation coefficient are used to analyze the main influencing factors of productivity. The back propagation neural network optimized by particle swarm optimization was employed to develop a productivity prediction model (PSO-BP model) for offshore deep and ultra-deep tight reservoirs. The actual test well data of the oilfield are substituted into this model. The analysis results of the example application yielded an RMSE of 0.032, an MAE of 1.209, and an R2 value of 0.919. Compared with traditional productivity prediction methods, this study concludes that the model is both reasonable and practical. The calculation speed is faster and the calculation result is more accurate, which can greatly reduce productivity errors. The model constructed in this paper is well suited for predicting the productivity of tight reservoirs within the WZ block. It offers substantial guidance for predicting the productivity of similar reservoirs and supports the economical and efficient development of petroleum reservoirs. [ABSTRACT FROM AUTHOR]

Taking a slope as a unit, the regional vulnerability assessment based on potential disaster intensity is one of the important problems to be solved urgently. In this paper, the city of Xiangxiang in Hunan is selected as the research area. On the basis of susceptibility regionalization with the weighted information value method, the elevation, slope height, slope, slope direction and monthly average rainfall of the highest prone points of slope units are extract one by one as the characteristic parameters, which are put into the BP neural network, PSO-BP neural network, random forest and support vector machine model, respectively. A landslide volume prediction model based on BP neural network algorithm optimized by PSO is constructed through training and precision test comparison. A comprehensive vulnerability evaluation model is established with disaster volume as disaster intensity index and building density, population density and property density as vulnerability indexes. Regional vulnerability evaluation based on potential disaster intensity is carried out for the study area. The divisions of high-vulnerable areas (1.5% of the total area), medium-vulnerable areas (28.5% of the total area) and low-vulnerable areas (70% of the total area) are completed, which realize the organic combination of the disaster intensity of the disaster-causing body and the vulnerability of the disaster-bearing body in the process of regional vulnerability evaluation, and enhance the objectivity and scientific nature of the evaluation.

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Asphalt mixture is a composite material with a complex multiphase dispersed system, and its macroscopic mechanical behavior is inherently related to the microstructure characteristics. To investigate the low-temperature crack resistance degradation law of polyester fiber asphalt mixture under different dry-wet cycling conditions, six polyester fiber contents (0%, 0.3%, 0.35%, 0.4%, 0.45%, and 0.5%) were dry-blended into SMA-13 asphalt mixture to prepare fiber-reinforced SCB specimens with pre-notches. Semi-circular bending tests were conducted to test the crack resistance performance of the fiber asphalt mixture after 0, 2, 4, 6, and 8 dry-wet cycles. The experimental results show that the crack resistance performance of specimens with different fiber contents decreases with the increase of dry-wet cycling times, and the influence of salt-dry-wet coupling is greater than that of water-dry-wet coupling. Under the same conditions, the crack resistance index (CRI) increases first and then decreases with the increase of polyester fiber content, reaching its maximum at a polyester fiber content of 0.4%. When the fiber is added at 0.5%, the agglomeration of polyester fibers restricts the low-temperature performance of the specimen. In addition, the DIC technology is used to analyze the trend of horizontal strain variation of specimens after different dry-wet cycles. The results indicate that with the increase of the cycle period, the strain concentration area becomes more apparent, and in the destruction stage, the full-field horizontal strain Exx in the crack concentration zone gradually increases, while the crack resistance decreases. Finally, addressing the limitations of traditional BP neural networks in solving nonlinear problems, a particle swarm optimization algorithm is proposed to optimize the BP neural network, and a PSO-BP neural network model is constructed. Through the analysis of evaluation indicators, it is found that the PSO-BP neural network has improved generalization ability compared to the traditional BP neural network model, and overfitting is reduced, making it an effective tool for predicting the low-temperature performance of polyester fiber asphalt mixtures.

Abstract The remanufacturing industry is experiencing significant growth, resulting in a substantial increase in its carbon footprint. Unfortunately, this issue is not receiving adequate attention at present. Moreover, existing methods for analyzing carbon footprints in remanufacturing systems are inefficient and inaccurate due to the complex relationship between carbon emissions and the dynamic nature of the systems. In this paper, we propose a multisource data‐driven approach for carbon footprint analysis of remanufacturing systems. First, we examine the relationship between carbon emissions and the uncertainty surrounding end‐of‐life (EOL) product components, as well as the corresponding resource recovery strategies, such as reuse, recycle, and remanufacture. This analysis allows us to define the carbon footprint boundary of remanufacturing systems. Next, we establish a carbon footprint model for recycling strategies using recycling data. Additionally, we propose a carbon footprint model for remanufacture strategies by utilizing the back propagation neural network based on particle swarm optimization algorithm and data from the materials flow, energy flow, and waste flow of these strategies. By combining these models, we can accurately calculate the carbon footprint of remanufacturing systems. To demonstrate the effectiveness of our approach, we present a case study on the remanufacturing of an EOL automobile engine. This case study showcases how our methodology can be utilized as a valuable tool for analyzing carbon footprints and identifying strategies to reduce carbon emissions in remanufacturing systems.

Abrasive disc grinding is currently a key manufacturing process to achieve better accuracy and high-quality surfaces of TC17 components. Grinding force, which results from the friction and elastic–plastic deformation during the contact and interaction between the abrasive grains and the workpiece, is a critical parameter that represents the grinding accuracy and efficiency. In order to understand the influence factors of grinding force, the characteristics of the flexible abrasive disc grinding process were studied. Considering the contact state between the abrasive tool and the workpiece, the theoretical model of normal grinding force was established in detail, from macro- and micro-perspectives. By conducting single-factor and orthogonal grinding experiments of TC17 components, the influence of different process parameters on the normal grinding force was revealed. The normal grinding force prediction models of the abrasive disc grinding process were developed based on the Box–Behnken design (BBD) and particle swarm optimization–back propagation (PSO-BP) neural networks, respectively. The results showed that the normal grinding force was negatively correlated with the disc rotational speed, and positively correlated with the contact angle, grinding depth, and feed rate, and the interaction of the factor feed rate and grinding depth was the more influential factor. Both the BBD and PSO-BP force models had good reliability and accuracy, and the mean absolute error (MAE) and mean relative error (MRE) of the above two prediction models were 0.22 N and 0.16 N, and 13.3% and 10.9%, respectively. [ABSTRACT FROM AUTHOR]

Based on particle swarm optimization-back propagation (PSO-BP) neural network, a novel inverse synthetic aperture radar (ISAR) image quality assessment (IQA) method is proposed to address the challenge of selecting high-quality ISAR images from a large number of images. This method incorporates six assessment metrics: energy gradient (EG), effective area ratio of image (EARI), image contrast (IC), image entropy (IE), equivalent number of looks (ENL), and signal-to-noise ratio (SNR) to evaluate the quality of ISAR images. The PSO-BP neural network is employed to fit the relationship between these metrics and ISAR image quality. Experimental results show that the new method is effective in ISAR-IQA under different imaging conditions and various attitudes, and closely matches the subjective evaluation of experts. [ABSTRACT FROM AUTHOR]

The remanufacturing industry is experiencing significant growth, resulting in a substantial increase in its carbon footprint. Unfortunately, this issue is not receiving adequate attention at present. Moreover, existing methods for analyzing carbon footprints in remanufacturing systems are inefficient and inaccurate due to the complex relationship between carbon emissions and the dynamic nature of the systems. In this paper, we propose a multisource data‐driven approach for carbon footprint analysis of remanufacturing systems. First, we examine the relationship between carbon emissions and the uncertainty surrounding end‐of‐life (EOL) product components, as well as the corresponding resource recovery strategies, such as reuse, recycle, and remanufacture. This analysis allows us to define the carbon footprint boundary of remanufacturing systems. Next, we establish a carbon footprint model for recycling strategies using recycling data. Additionally, we propose a carbon footprint model for remanufacture strategies by utilizing the back propagation neural network based on particle swarm optimization algorithm and data from the materials flow, energy flow, and waste flow of these strategies. By combining these models, we can accurately calculate the carbon footprint of remanufacturing systems. To demonstrate the effectiveness of our approach, we present a case study on the remanufacturing of an EOL automobile engine. This case study showcases how our methodology can be utilized as a valuable tool for analyzing carbon footprints and identifying strategies to reduce carbon emissions in remanufacturing systems. [ABSTRACT FROM AUTHOR]

In order to make the temporary support force better adapt to the mine pressure and improve the support capacity of the support, taking the dual self-moving temporary support as the research object, an adaptive control method of temporary support force based on particle swarm optimization (PSO) - BP neural network is proposed. The initial weights of the BP neural network are optimized by using the global search capability and fast convergence features of the PSO algorithm to improve the rate of convergence of the BP neural network. Then, the optimized BP neural network is used to achieve online self-adjustment of PID parameters. The PSO-BP neural network is constructed to optimize the PID controller. This enables the temporary support force to reach the predetermined value more quickly and accurately, achieving adaptive control of the temporary support force. It avoids damage to the roof due to the mismatch between support force and roof pressure. The expected initial support force of the temporary support is simulated using unit step signals for experimental verification. The results show that compared with the BP neural network optimized PID controller and traditional PID controller, the PSO-BP neural network optimized PID controller can achieve the expected initial support force faster and more accurately. The adjustment time is only 0.5 s and there is almost no overshoot. Based on actual geological conditions, the roof pressure on the support during excavation support is simulated. The adaptive control effect of three controllers for support force is studied. The results show that under the control of the PSO-BP neural network optimized PID controller, the system error is only 0.02 MPa, with the smallest error and the best control effect.

Energy is an important global issue at present, and reducing energy consumption of public organizations can promote the development of a low-carbon economy and low-carbon society, which is of immense significance to both economic and social development. In this paper, we gather dynamic energy consumption data from public institutions using the energy consumption dynamic monitoring platform, preprocess the information, and apply the 3-sigma criterion method to identify abnormal energy consumption points in these institutions. We then propose an energy consumption prediction model based on the PSO-BP neural network, use the Markov model to backtest historical energy consumption data, correct the model’s prediction results, and construct an energy consumption prediction system for public organizations. The study shows that the prediction effect of this system on energy consumption is better than that of the RS model and DS model for 1 hour and 24 hours in advance. This paper’s energy consumption prediction system effectively and timely detects and diagnoses energy consumption anomalies in public organizations’ operations, thereby supporting their energy conservation management. This paper lays the foundation for the establishment of an energy consumption prediction system and the study of energy-saving strategies. It can provide the basis and strong guidance for the optimization of energy-saving operations in public institutions.

In this study, polyvinyl alcohol (PVA) fibers and nanoparticles were incorporated to enhance the durability of geopolymer mortar (GM) with metakaolin (MK) and fly ash (FA). The dosage of nano-SiO2 (NS) was 0–2.5% and that of PVA fiber was 0–1.2%. The durability of GM includes resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion. Compared with the single BP neural network (BPNN) model, a particle swarm optimized BPNN (PSO-BPNN) model was utilized to predict the resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion of GMs with different dosages of nanoparticles and PVA fibers. In the model, the dosage of NS, PVA fiber, FA, and MK were used as input layers, and the durability parameters of electric flux, mass loss, and compressive strength loss of GMs were used as output layers. The result exhibits that the root mean square errors (RMSEs) of BPNN for resistance to chloride ion penetration, freeze–thaw cycles, and sulfate erosion of GM mixed with nanoparticles and PVA fibers are 145.39, 6.43, and 2.19, whereas RMSEs obtained from PSO-BPNN are 76.33, 2.87, and 1.03, respectively. The NN optimized by particle swarm algorithm has better prediction accuracy. The PSO-BPNN can be utilized for estimating durability of GM reinforced by NS and PVA fiber, which can provide a guide for the proportion design of GM with PVA fiber and NS as well as for the engineering practice in the future.

In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire. [ABSTRACT FROM AUTHOR]

In full-section excavated tunnel construction, the abnormal deformation of the surrounding rock, such as arch cracking and surrounding rock collapse, is often caused by insufficient timely support and over-excavation, which brings serious consequences to the project. In this paper, the geomechanical model is established to investigate the influence of different indexes on surrounding rock deformation. The deformation of surrounding rock can be divided into three stages, among which the earlier deformation caused by the construction in front of the monitoring section in the 1st stage accounts for 18% of the total deformation. The deformation caused by tunnel excavation in the second stage occupies 72%, and the lagging deformation in the third stage accounts for 10% of the total deformation. Furthermore, the critical indicators are screened out by the rough set algorithm. Finally, the prediction model of surrounding rock deformation of the full-section excavated tunnel is established based on the PSO-BP neural network. The validation results indicated that the prediction results from the integration model are in good agreement with the field observation results, and the prediction efficiency and accuracy are better than those of the numerical simulation and the single BP neural network. This study fully used tunnel monitoring data and proposed a deformation prediction model by investigating the correlation characteristics of the deformation data, which provides an essential reference for similar engineering research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coal has played an important role in the economies of many countries worldwide, which has resulted in increased surface and underground mining in countries with large coal reserves, such as China and the United States. However, coal mining is subject to frequent accidents and predictable risks that have, in some instances, led to the loss of lives, disabilities, equipment damage, etc. The assessment of risk factors in underground mines is therefore considered a commendable initiative. Therefore, this research aimed to develop an efficient model for assessing and predicting safety risk factors in underground mines using existing data from the Xiaonan coal mine. A model for evaluating safety risks in underground coal mines was developed based on the optimized particle swarm optimization-backpropagation (PSO-BP) neural network. The results showed that the PSO-BP neural network model for safety risk assessment in underground coal mines was the most reliable and effective, with MSE, MAPE, and R2 values of 2.0 × 10−4, 4.3, and 0.92, respectively. Therefore, the study proposed the neural network model PSO-BP for underground coal mine safety risk assessment. The results of this study can be adopted by decision-makers for evaluating and predicting risk factors in underground coal mines. [ABSTRACT FROM AUTHOR]

The upgrading of human capital caused by education is significant to regional development. Reasonable predictions of the degree of advanced human capital in different regions are effective for formulating reasonable talent policies and accelerating regional coordinated development. The BP neural network is a widely used prediction technology. PSO-BP neural network has good global search ability, which can accelerate the convergence speed of traditional BP neural network, which is suitable for forecasting larger data. The study takes the provincial data of China from 2005 to 2019 as an example, using PSO-BP neural network algorithm to predict the advanced level of human capital through the influencing factors filtered by OLS regression. The results show that: (1) Innovation ability and urbanization can play a decisive role in advanced human capital filtered by OLS regression; (2) The results of predicting the development trend of advanced human capital in the Beijing–Tianjin–Hebei region in 2020–2025 through the PSO-BP neural network have showed that there is still a large gap between the senior human capital stock in Hebei-Beijing-Tianjin in terms of total and per capita in 2020–2025 compared with other regions in east of China; (3) Giving full attention to elaborate the positive role of economic quality and quantity development are suitable for narrowing the difference of advanced human capital in this region. Through the method of OLS-BP-neural network, this study explores the gap and influencing factors of the Beijing–Tianjin–Hebei region, excavates the reasons for the huge gradient difference in the development of this region, and extends the machine learning prediction method to the analysis of the advanced level of human capital and the research of narrowing the regional development gap. [ABSTRACT FROM AUTHOR]

Abrasive disc grinding is currently a key manufacturing process to achieve better accuracy and high-quality surfaces of TC17 components. Grinding force, which results from the friction and elastic–plastic deformation during the contact and interaction between the abrasive grains and the workpiece, is a critical parameter that represents the grinding accuracy and efficiency. In order to understand the influence factors of grinding force, the characteristics of the flexible abrasive disc grinding process were studied. Considering the contact state between the abrasive tool and the workpiece, the theoretical model of normal grinding force was established in detail, from macro- and micro-perspectives. By conducting single-factor and orthogonal grinding experiments of TC17 components, the influence of different process parameters on the normal grinding force was revealed. The normal grinding force prediction models of the abrasive disc grinding process were developed based on the Box–Behnken design (BBD) and particle swarm optimization–back propagation (PSO-BP) neural networks, respectively. The results showed that the normal grinding force was negatively correlated with the disc rotational speed, and positively correlated with the contact angle, grinding depth, and feed rate, and the interaction of the factor feed rate and grinding depth was the more influential factor. Both the BBD and PSO-BP force models had good reliability and accuracy, and the mean absolute error (MAE) and mean relative error (MRE) of the above two prediction models were 0.22 N and 0.16 N, and 13.3% and 10.9%, respectively.

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to understand consumers' cognition of clothing style and design clothing products more in line with people's emotional needs, a garment style perceptual image prediction model based on PSO-BP neural network was constructed by taking professional dress as an example. Firstly, the professional dress samples were screened and the style design elements were deconstructed and coded. The Kansei engineering theory and factor analysis method were used to determine the representative adjectives, so as to reduce the cognitive dimension of the target users for the style characteristics and perceptual image of the dress. Then, using the sample style design element code as the input layer and the user's perceptual image evaluation score as the output layer, the PSO-BP neural network's perceptual image prediction model for professional dress styles is constructed. Finally, the sample data were input into the PSO-BP model, BP neural network and GA-BP model for simulation and calculation, and the error analysis of the results proved that the PSO-BP prediction model is effective and advanced. Designers can use this model to quickly transform customers' perceptual needs with dress style design elements, so as to improve the scientificity of design decision-making and better meet customer needs. [ABSTRACT FROM AUTHOR]

Abstract The outliers in traffic flow represent the anomalies or emergencies in the road. The detection and research of outliers will help to reveal the mechanism of such events. Aiming at the problem of outlier detection in urban traffic flow, this paper innovatively proposes a highly efficient traffic outlier detection framework based on the study of road traffic flow patterns. The main research works are as follows: (1) data pre‐processing, the road traffic flow matrix of the roads is calculated based on the collected GPS data, the non‐negative matrix factorisation algorithm is chosen to reduce the dimension of the matrix. (2) Road traffic flow pattern extraction, the fuzzy C‐means clustering algorithm with the Optimal k‐cluster centre (K‐FCM) is adopted to cluster the roads with the same road traffic flow pattern. (3) Outlier detection model training and evaluation, kernel density estimation is introduced to fit the probability density of roads traffic flow matrices which are used to train the back propagation neural network based on particle swarm optimisation to obtain the outlier detection and evaluation model, and a threshold is introduced to optimise the precision and recall of the model. The experimental results show that: the average precision and recall of the proposed method in this paper are 95.38% and 96.23%, respectively, and the average detection time is 28.4 seconds. The method has high accuracy, high efficiency and good practical significance.

In this paper, we propose a novel shape-sensing method based on deep learning with a multi-core optical fiber for the accurate shape-sensing of catheters and guidewires. Firstly, we designed a catheter with embedded multi-core fiber containing three sensing outer cores and one temperature compensation middle core. Then, we analyzed the relationship between the central wavelength shift, the curvature of the multi-core Fiber Bragg Grating (FBG), and temperature compensation methods to establish a Particle Swarm Optimization (PSO) BP neural network-based catheter shape sensing method. Finally, experiments were conducted in both constant and variable temperature environments to validate the method. The average and maximum distance errors of the PSO-BP neural network were 0.57 and 1.33 mm, respectively, under constant temperature conditions, and 0.36 and 0.96 mm, respectively, under variable temperature conditions. This well-sensed catheter shape demonstrates the effectiveness of the shape-sensing method proposed in this paper and its potential applications in real surgical catheters and guidewire.

The artificial neural network method has been widely applied to the performance prediction of fillers and evaporative coolers, but its application to the dew point indirect evaporative coolers is rare. To fill this research gap, a novel performance prediction model for dew point indirect evaporative cooler based on back propagation neural network was established using Matlab2018. Simulation based on the test date in the moderately humid region of Yulin City (Shaanxi Province, China) finds that: the root mean square error of the evaporation efficiency of the back propagation model is 3.1367, and the r2 is 0.9659, which is within the acceptable error range. However, the relative error of individual data (sample 7) is a little bit large, which is close to 10%. In order to improve the accuracy of the back propagation model, an optimized model based on particle swarm optimization was established. The relative error of the optimized model is generally smaller than that of the BP neural network especially for sample 7. It is concluded that the optimized artificial neural network is more suitable for solving the performance prediction problem of dew point indirect evaporative cooling units. [ABSTRACT FROM AUTHOR]

Coal has played an important role in the economies of many countries worldwide, which has resulted in increased surface and underground mining in countries with large coal reserves, such as China and the United States. However, coal mining is subject to frequent accidents and predictable risks that have, in some instances, led to the loss of lives, disabilities, equipment damage, etc. The assessment of risk factors in underground mines is therefore considered a commendable initiative. Therefore, this research aimed to develop an efficient model for assessing and predicting safety risk factors in underground mines using existing data from the Xiaonan coal mine. A model for evaluating safety risks in underground coal mines was developed based on the optimized particle swarm optimization-backpropagation (PSO-BP) neural network. The results showed that the PSO-BP neural network model for safety risk assessment in underground coal mines was the most reliable and effective, with MSE, MAPE, and R2 values of 2.0 × 10−4, 4.3, and 0.92, respectively. Therefore, the study proposed the neural network model PSO-BP for underground coal mine safety risk assessment. The results of this study can be adopted by decision-makers for evaluating and predicting risk factors in underground coal mines.

The yield performances and failure mode of reinforced concrete (RC) columns, which are critical structural performances to the design and research of engineering structures, have a significant impact on the dynamic response, the performance level, and the design of seismic ductility. The traditional empirical theoretical method used to predict the yield performances and failure mode leads to large dispersions in most cases. To better estimate the yield performances and failure mode of RC columns, this paper developed a novel neural network method. Empirical theoretical models are used to determine the input parameters of the neural network by analyzing the factors that affect the yield performance and failure mode of RC columns, and the rationality of these parameters is verified by sensitivity analysis. The back-propagation (BP) neural network method was adopted. The influence of the number of hidden neurons was studied to improve the model accuracy. Comparative analysis revealed that the prediction results of the neural network are in good agreement with the experimental results and are more accurate than other traditional empirical theoretical models. The initial parameters were optimized using particle swarm optimization (PSO), which has been proven to be superior to the genetic algorithm (GA) and sparrow search algorithm (SSA) optimization methods in terms of effectiveness and computation time. The high generalization ability of the prediction model was calibrated using the test and validation sets and another eight additional sets of experimental data. The proposed method provides a new way to predict the structural performance under seismic actions when experimental data are insufficient. [ABSTRACT FROM AUTHOR]

Thin-walled structures (TWS) were widely used in engineering equipment, and may be subjected to impact loads to produce different degrees of structural damage during application. However, it is a difficult problem to determine the impact load conditions for these structural damages. In this study, we developed a novel method of identifying the impact load condition of the thin-walled structure damage, which is based on particle swarm optimization-backpropagation (PSO-BP) neural network. First, the known impact position and velocity are applied to the finite element model (FEM) of the TWS to produce permanent plastic deformation, and to fit the characteristic shape of the deformation is needed by invoking the multivariate polynomial function. Then, the method is devoted to build a basic data set. With impact position and velocity as input and function coefficients as output, a model of extended PSO-BP neural network is established. Besides, the basic sample set is expanded to solve the lack of samples. Ultimately, utilizing the expanded total sample set as training data, function coefficients, impact position and velocity will be outputted. On the basis of the known functional coefficients of deformed surfaces, a model of predictive PSO-BP neural network is established and predicted. Furthermore, we predicted the collision position and velocity using a conventional BP neural network in the same way. Finally, the predicted impact position and velocity is compared with the analysis results of the FEM, which verifies that the PSO-BP neural network algorithm has high accuracy. [ABSTRACT FROM AUTHOR]

The outliers in traffic flow represent the anomalies or emergencies in the road. The detection and research of outliers will help to reveal the mechanism of such events. Aiming at the problem of outlier detection in urban traffic flow, this paper innovatively proposes a highly efficient traffic outlier detection framework based on the study of road traffic flow patterns. The main research works are as follows: (1) data pre‐processing, the road traffic flow matrix of the roads is calculated based on the collected GPS data, the non‐negative matrix factorisation algorithm is chosen to reduce the dimension of the matrix. (2) Road traffic flow pattern extraction, the fuzzy C‐means clustering algorithm with the Optimal k‐cluster centre (K‐FCM) is adopted to cluster the roads with the same road traffic flow pattern. (3) Outlier detection model training and evaluation, kernel density estimation is introduced to fit the probability density of roads traffic flow matrices which are used to train the back propagation neural network based on particle swarm optimisation to obtain the outlier detection and evaluation model, and a threshold is introduced to optimise the precision and recall of the model. The experimental results show that: the average precision and recall of the proposed method in this paper are 95.38% and 96.23%, respectively, and the average detection time is 28.4 seconds. The method has high accuracy, high efficiency and good practical significance. [ABSTRACT FROM AUTHOR]

A BP （Back-Propagation） neural network model optimized by PSO （ Particle Swarm Optimization） and based on data preprocessing is proposed for sag prediction of overhead transmission lines, in order to solve the susceptibility of sag computation to measured data of temperature, wind speed, span and other parameters. For the missing data in collected database, the Synthetic Minority Oversampling Technique （SMOTE） was used to synthesize unbalanced samples. The proposed PSO-BP neural network was trained and tested by data obtained in different working environments. Experiments were carried out to verify the effectiveness of the proposed approach. The results showed that, compared with traditional BP neural network, the proposed model has a significant reduction in the relative error of sag prediction, and can accelerate the training speed as well as improve the sag prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the ultimate crushing displacement Y1 and load Y2 of the coated fertilizer granules were obtained by uniaxial compression test as 0.450 mm and 58.668 N, respectively. The Plackett-Burman and Steepest ascent tests were taken to determine factors that had significant effects on the results and their ranges of values, respectively. Finally, the Particle Swarm Optimization - Back Propagation (PSO-BP) neural network was trained, and the correlation coefficients of training, validation, testing and overall performance were obtained as 0.98057, 0.95781, 0.96724 and 0.97459, respectively. The Y1 and Y2 are 0.450 mm and 58.703N, with a relative error of 0.06% from the actual value. [ABSTRACT FROM AUTHOR]

The isothermal compression tests of Ti-2.7Cu alloy were tested to study the hot deformation behavior in temperature range of 740-890℃ and strain rate range of 0.001-10s-1 on a Gleeble-3500 thermomechanical simulator. Constitutive model based on strain compensation was established by the Arrhenius hyperbolic sine function equation, and set up a constitutive equation for PSO-BP neural network. The results show that the flow stress is more sensitive to deformation temperature and strain rate, the flow stress is decreased with the increase of deformation temperature and decrease of strain rate; the flow stress curves present stable states in high temperature and low strain rate. For a constitutive equation based on strain compensation, the data points with the predicted error less than 15% account for 85.28% of all test data by error calculation; and for the constitutive equation based on PSO-BP neural network, the data points with the predicted error less than 15% account for 96.67% of all test data. PSO-BP neural network model has higher accuracy, it can better predict the flow stress of Ti-2.7Cu at elevated temperature.

This paper proposes a method for predicting concentricity and perpendicularity based on PSO-BP neural network in order to solve the problem of low accuracy for aero engine multistage rotors assembly. The influence factors of error propagation in the assembly are analyzed based on the characteristics of rotor structure and assembly process. And neural networks for predicting concentricity and perpendicularity of multistage rotors assembly are established. The particle swarm algorithm is used to optimize the hyperparameters of the neural network and the optimal hyperparameters can be obtained. In order to verify the effectiveness of the concentricity and perpendicularity prediction method proposed in this paper, experiments are carried out for four rotors assembly with precision rotary measuring instrument. The results show that for the 30 groups of testing samples, the average deviations of concentricity and perpendicularity by PSO-BP neural network prediction method are 1.0 μm and 0.6 μm, respectively. The prediction accuracy of concentricity and perpendicularity of final assembly are improved by 4.5 μm and 2.6 μm, respectively, compared with the traditional assembly method. The proposed method in this paper can be used not only for the guidance of multistage rotors assembly of aero engine, but also for the tolerance allocation in the design process.

The artificial neural network method has been widely applied to the performance prediction of fillers and evaporative coolers, but its application to the dew point indirect evaporative coolers is rare. To fill this research gap, a novel performance prediction model for dew point indirect evaporative cooler based on back propagation neural network was established using Matlab2018. Simulation based on the test date in the moderately humid region of Yulin City (Shaanxi Province, China) finds that: the root mean square error of the evaporation efficiency of the back propagation model is 3.1367, and the r2 is 0.9659, which is within the acceptable error range. However, the relative error of individual data (sample 7) is a little bit large, which is close to 10%. In order to improve the accuracy of the back propagation model, an optimized model based on particle swarm optimization was established. The relative error of the optimized model is generally smaller than that of the BP neural network especially for sample 7. It is concluded that the optimized artificial neural network is more suitable for solving the performance prediction problem of dew point indirect evaporative cooling units.

A model of back propagation( BP ) and particle swarm optimization( PSO) neural network of Cu-Ag alloy based on size effect were established. The neural network models were applied to Cu-Ag alloy microwire tensile strength prediction. Experimental study of micro-wire drawing of the alloy with different diameter and silver content shows that the cast rod mainly consists of eutectic dendritic microstructure. The dendritic structure disappears when the strain reaches 98. 06%. The microstructure of the alloy is dominated by small and homogeneous equiaxed grains. The tensile strength of the alloy increases with decreasing wire diameter. Significant size effect is found. When the wire diameter is smaller than 1 mm the tensile strength of the alloy increases dramatically. The optimal topology of neural network model is 2-10-1. The correlation coefficients of the two neuron network model sample data are 0. 794 and 0. 907 respectively. The maximum relative error absolute value of the PSO-BP artificial neural network model test sample is 3. 3% which can predict the tensile strength of the Cu-Ag alloy. [ABSTRACT FROM AUTHOR]

The yield performances and failure mode of reinforced concrete (RC) columns, which are critical structural performances to the design and research of engineering structures, have a significant impact on the dynamic response, the performance level, and the design of seismic ductility. The traditional empirical theoretical method used to predict the yield performances and failure mode leads to large dispersions in most cases. To better estimate the yield performances and failure mode of RC columns, this paper developed a novel neural network method. Empirical theoretical models are used to determine the input parameters of the neural network by analyzing the factors that affect the yield performance and failure mode of RC columns, and the rationality of these parameters is verified by sensitivity analysis. The back-propagation (BP) neural network method was adopted. The influence of the number of hidden neurons was studied to improve the model accuracy. Comparative analysis revealed that the prediction results of the neural network are in good agreement with the experimental results and are more accurate than other traditional empirical theoretical models. The initial parameters were optimized using particle swarm optimization (PSO), which has been proven to be superior to the genetic algorithm (GA) and sparrow search algorithm (SSA) optimization methods in terms of effectiveness and computation time. The high generalization ability of the prediction model was calibrated using the test and validation sets and another eight additional sets of experimental data. The proposed method provides a new way to predict the structural performance under seismic actions when experimental data are insufficient.

Aiming at the typical characteristics of natural rainfall in alpine areas and the structure of the concentrator surface, based on the design scheme of the trough system test platform, the optical performance and cleaning characteristics of the mirror with rainfall intensity and concentrator tilt angle as variables are studied experimentally. The results show that small-scale rainfall has a critical tilt angle of positive and negative cleaning effect on transverse region1. Lower than this tilt angle causes pollution to the mirror surface. Rainfall above medium intensity has a positive cleaning effect on all mirror regions. The cleaning ability is the highest under the rainfall of 60° tilt angle of 38.9 mm. The bottom-up enhanced cleaning law in the transverse region is more significant under high-intensity rainfall, and the concentrator tilt angle is more sensitive to dust desorption than rainfall intensity. The average relative error and average root mean square error of the predicted reflectivity of the global mirror are 3.82% and 0.061 respectively, and the fitting accuracy is high. It provides theoretical guidance for the application of dust removal schemes and prediction of the optical performance of trough solar system in alpine areas. [ABSTRACT FROM AUTHOR]

• This paper proposes hybrid filtering and fluctuating range compression factor Q for processing RSSI data in buried environments, significantly reducing RSSI fluctuations. • Using the processed RSSI data, we propose HFQBP, a novel ranging method showcasing high accuracy and adaptability in complex environments. • We've improved prior localization methods, introducing two novel approaches: Optimized Multilateration (OM) and Weighted Six-Point Centroid (WSC) algorithms for complex environment. • We simulated a post-earthquake burial environment to validate the proposed localization solution. With the frequent occurrence of global earthquake disasters in recent years, quickly and accurately locating trapped people is the key to reducing the mortality rate of earthquake disasters. This paper proposes a cell phone localization solution for trapped people that can be rapidly deployed after an earthquake. This solution locates the buried cell phone by collecting its received WiFi signal strength indication (RSSI) data. Reducing the fluctuation of RSSI value is crucial to accurately locating the buried cell phone. We filter RSSI data outliers by hybrid filtering and propose a fluctuation range compression factor Q to reduce the fluctuation range of RSSI data. In order to adapt to the compressed RSSI data fluctuation range, the processed RSSI data were grouped. The grouped data are then imported into the PSO-BP neural network training to build the ranging model. So that there is a mapping relationship between RSSI data and trapped distance. The established ranging model is denoted as the HFQBP ranging model. After establishing the ranging model, the cell phone is localized by an Optimized Multilateration (OM) algorithm. For simplicity, this algorithm is named HFQBP-OM. In the experimental phase, we combined the method proposed in this paper with other methods, resulting in three different localization algorithms: HFQBP-WSC, PL-OM, and PL-WSC. And compared with the algorithms in this paper. The results indicate that the algorithm HFQBP-OM proposed in this paper has improved the total average accuracy of the nine localization points by 34.7%, 54.8%, and 58.9% compared to HFQBP-WSC, PL-OM, and PL-WSC, respectively. The HFQBP-OM algorithm has higher accuracy and stability. Also, it should be noted that the localization solution proposed in this paper uses smartphones connected to WiFi probes to collect RSSI data of the WiFi signal of the buried cell phone. Use a laptop computer to solve the localization coordinates. Smartphones and laptops are so popular that WiFi probes are the only special equipment needed. WiFi probe is inexpensive. It reduces the difficulty of implementing the localization solution in this paper and increases the generalizability of the solution. [ABSTRACT FROM AUTHOR]

• The importance of the variables on optimal discharge pressure was studied. • PSO-BP neural network for optimal discharge pressure prediction was developed. • Influence of number of hidden layer and particle cycles was investigated. • Novel prediction results were compared with current correlations. • New predictions were given for optimal discharge pressure in CO 2 heat pumps. Discharge pressure is an important factor that heavily affects the system COP in the transcritical CO 2 heat pump. In most cases, it is commonly confirmed by the empirical correlations or calculated by the mathematical model according to a single operation condition, thus leading to the prediction error or lengthy time. In this paper, a novel model using the statistical method known as the group method of data handling-type (GMDH) and PSO-BP-type (Particle-Swarm-Optimization and Back-Propagation) neural network was developed to predict the optimal discharge pressure. The relevance of all the parameters to the optimal discharge pressure was investigated orderly. Results showed that the new model had the highest accuracy compared to the current correlations. The relative error was around 1.6% while the error of traditional methods ranged from 11.1% to 44.9%. Therefore, the CO 2 heat pump could work better in the optimal COP operation condition with the novel statistical model. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the traditional boring bar, a boring bar with friction damper is proposed in the paper. Firstly, the frequency response under different pressures is computed primarily based on the theory, which shows that the proposed boring bar has a certain vibration reduction effect. Secondly, the finite element model of the boring bar is built, and the first 6-order modes are computed, whose results are compared with the experimental value. As a result, the virtual reality of the boring bar is achieved. They are consistent with each other, which show that the finite element model is reliable. Then, the experimental cutting process of the boring bar is researched, which is compared with the simulation model with good coincidence. It is found from the result that the cutting simulation model of the boring bar is effective. Later, based on the verified simulation model, the positive pressure between the friction vibrator and boring bar, cutting speed, feed rate, back cutting depth and other parameters are changed to study the vibration reduction effects of the boring bar with friction damper. PSO (particle swarm optimization)-BP (backpropagation) neural network is then used to optimize the cutting process of the boring bar, and the optimal cutting parameters can be obtained. Finally, these optimized parameters are applied in the boring bar, the vibration reduction effect of the boring bar is verified by means of experiments, and the corresponding result shows that the proposed optimization in this paper is feasible. We can obtain higher quality work piece when we use this boring bar in the actual engineering. [ABSTRACT FROM AUTHOR]

Due to the intermittence and fluctuation of wind power generation, accurate forecasting of wind power is of great significance for the safe and stable operation of wind power integrated system. In the paper, a new wind power forecasting method combining particle swarm optimization (PSO) and back-propagation (BP) neural network is proposed. BP neural network structure was constructed according the number of input data, and PSO algorithm is used to get the optimal initial weights and biases of BP neural network, which can effectively overcome the shortcoming of BP neural network that it is easy to fall into local optimal solution, and increase the convergence speed of BP neural network. Considering the integrity of training data, the PSO-BP neural network was trained to use full year data in 2011. The original BP, PSO-BP, GA-BP and wavelet-BP neural network is applied for 6-h, 1- day, 3-day wind power forecasting in May and December of 2012, respectively. The compared results show that the mean absolute error(MAE) and the root mean square error(RMSE) of wind power forecasting based on PSO-BP neural network are clearly less than that based on original BP, GA-BP and wavelet-BP neural network. [ABSTRACT FROM AUTHOR]

Firstly, this paper computed the static and dynamic characteristics of common boring bars and constrained damping boring bars respectively, and the correctness of the computational model in time-frequency domain was also validated by experiments. Modal frequencies of constrained damping boring bars were obviously more than those of common boring bars, which could effectively avoid structural resonance in low frequency and had an obvious advantage in improving anti-vibration performance of boring bars. The absolute value of the maximum vibration acceleration of common boring bars was 13.1 m/s², while the absolute value of the maximum vibration acceleration of constrained damping boring bars was 9.1 m/s². The maximum vibration acceleration decreased by 30.5 %. The maximum vibration displacement of common boring bars was 5.2 mm and corresponding frequency was 201 Hz. The maximum vibration displacement of constrained damping boring bars was 2.3 mm and corresponding frequency was 235 Hz. When the analyzed frequency was lower than the frequency with the maximum vibration displacement, the displacement spectrum of common boring bars had more peak values. Thus, it was clear that constrained damping boring bars had an obvious advantage in improving vibration characteristics. The impact of cutting speed, feed rate and back cutting depth on vibration characteristics was studied respectively. Results showed that the vibration of constrained damping boring bars gradually decreased with the increase of cutting speed and gradually increased with the increase of feed rate and back cutting depth. In addition, the amplitude and frequency of vibration displacement spectrum of boring bars were basically unchanged no matter how cutting parameters changed. In order to quickly predict the vibration characteristic, BP neural network and PSO-BP neural network were respectively used to predict the cutting process of boring bars. When the iteration number of BP neural network was 300, iterative error was 0.00015 which was far more than the set target error. When the iteration number of PSO-BP neural network was 215, iterative error was converged to the set target error. Therefore, PSO-BP neural network had an obvious advantage in predicting the cutting process of boring bars. In addition, the predicted result of PSO-BP neural network was consistent with the experimental result, which showed that the neural network model in this paper was effective. [ABSTRACT FROM AUTHOR]

With the diversification and deepening of intelligent technology research, intelligent vehicles are gradually involved in entering the transportation life. When intelligent vehicles enter the actual road driving, the success of changing lanes directly determines the rate of safety problems occurring when driving. Therefore, combining with the current background of advocating low-carbon travel, the relevant technology of intelligent vehicles is improved and supplemented in terms of trajectory planning and the degree of impact on the environment, and a trajectory optimization method is proposed that considers the traffic impact and low-carbon of the vehicle when changing lanes. The method combines a vehicle dynamics model with intelligent vehicle operating conditions, the corresponding positional relationship is used to analyze and establish the corresponding safety domain. At the same time, the quintic polynomial model in the ideal state is improved, and the hexagonal polynomial model in the longitudinal direction is established. For the lane change is difficult to get the completion time and end position of the lane change in the PSO-BP neural network to solve the problem, to get the intelligent vehicle lane change trajectory cluster a dual-objective performance evaluation function is established, optimization of evaluation results using the properties of genetic algorithms. Taking car No. 689 in the NGSIM data as an example, the established trajectory planning model and algorithm are used to solve the problem, and the final lane-changing trajectory equation is obtained. After the simulation of the obtained data, a more suitable trajectory curve is obtained when the vehicle is running in the lane change, which shows that the lane change trajectory optimization method can solve the lane change problem well. Then the study is mainly applied to the planning of trajectories required by intelligent vehicles when lane changing occurs on real roads. After knowing the parameters of the lane-changing vehicle before and after changing lanes, which can plan a safe and efficient lane change route for vehicles in real time. The two comprehensive indicators of sexuality reached a better value. [ABSTRACT FROM AUTHOR]

• The impact of simulated vehicle speeds, lighting fades and luminaire failures on traffic safety are fully considered. • The traffic safety factor is proposed for quantitative assessment of traffic safety in road tunnels. • The neural network is applied to the field of tunnel lighting. • The evaluation method proposed can provide a theoretical basis for the maintenance of tunnel lighting systems. The lighting service state of road tunnels has an important impact on traffic safety. To assess and predict traffic safety in road tunnels under different lighting service states, a traffic safety assessment system of road tunnels was innovatively established by using visual recognition clarity data from 300 field experiments, which uses the proposed traffic safety factor to assess the traffic safety of road tunnels under different lighting service states. Then, the Particle Swarm Optimization (PSO) algorithm is used to optimize the Back Propagation (BP) neural network, and a new traffic safety intelligent prediction method in road tunnels was constructed to predict the traffic safety factor under different lighting service states. The results of the study show that there is a negative correlation between simulated vehicle speed and visual clarity and a positive correlation between lighting attenuation and visual clarity. There is a negative correlation between the number of luminaire failures and the visual recognition clarity. When the lighting attenuation is below 70% or four and above luminaires fail, the lighting service states of road tunnels can seriously threaten traffic safety. The PSO-BP neural network model can accurately predict the traffic safety factor. The critical value of the traffic safety factor is 0.5965. This means that the lighting service state poses a threat to traffic safety in road tunnels when the traffic safety factor is lower than this value. The results of the study can provide the basis for developing a safe and low-carbon tunnel lighting maintenance program. [ABSTRACT FROM AUTHOR]