Analysis and prediction of double-carriage train wheel wear based on SIMPACK and neural networks

Wang, Shuwen; Guo, Han; Zhang, Siyuan; Barton, D.C.; Brooks, Peter C.

doi:10.1177/16878132221078491

Cited by 20 publications

(11 citation statements)

References 27 publications

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“…For the training and detection of the BPNN algorithm model of CAPM for e-commerce financing in response to the “double carbon” policy, data normalization is usually carried out, that is, the meaningful data are processed into nondimensional data in the form of function calculation to maintain unity [ 31 ]. The main algorithms used are the maximum–minimum method and the mean-variance method.…”

Section: Principle and Model Establishment Of Bpnn Algorithm For The ...mentioning

confidence: 99%

Application of Capital Asset Pricing Model Based on BP Neural Network in E-commerce Financing

Geng

Guan

2022

Computational Intelligence and Neuroscience

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The study explores the risks and benefits of investors in e-commerce financing under the background of “double carbon” to maximize investors’ interests and reduce investment losses. The Back Propagation Neural Network (BPNN) algorithm model of e-commerce enterprise financing based on the Capital Asset Pricing Model (CAPM) is mainly studied. First, according to the worldwide literature, the theoretical concept and principle of the CAPM are deeply studied and analyzed. Then, from the perspective of “double carbon,” with the financing risk characteristics of listed companies responding to the “double carbon” policy as samples, the CAPM model of e-commerce financing under the BPNN algorithm is established. Next, the BPNN is used to input the financing samples of e-commerce enterprises and train the model. The verification experiment of the capital asset financing model of e-commerce enterprises is further conducted. The experimental results show that the model error is the smallest when the number of neurons in the hidden layer reaches about 20. Therefore, the number of neurons in the hidden layer of the model is set to 20. When the number of iterations in training reaches 3000, the financing risk model begins to show a convergence trend. Finally, it can be determined that the number of adaptive iterations of the model is 3000. When the learning rate is 0.03, the oscillation of the model is smaller and stabler, so the model learning rate is 0.03, and the final model error is only 9.96 × 10 − 8 . Based on this, e-commerce enterprises can achieve the purpose using this model to adjust the coefficient in financing in the future. The results have certain reference significance for e-commerce financing risk assessment under a “double carbon” background.

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Section: Principle and Model Establishment Of Bpnn Algorithm For The ...mentioning

confidence: 99%

Application of Capital Asset Pricing Model Based on BP Neural Network in E-commerce Financing

Geng

Guan

2022

Computational Intelligence and Neuroscience

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“…The values of

{A}_i

components depend on

\overrightarrow{a}

. Accordingly,

\left|{A}_i\right|>1

implies exploration, while

\left|{A}_i\right|\le 1

enforces exploitation 41 . The structure of GWO is depicted in Algorithm 1.…”

Section: Methodsmentioning

confidence: 99%

“…Nowadays, artificial intelligence (AI) is effectively employed to improve the human lifestyle in all aspects 35–46 . Researchers are improving AI‐related methods 47–52 .…”

Section: Introductionmentioning

confidence: 99%

An intelligent signboard to assign the speed limit on ways using ANN

Javadian

Hayati

Ghasemi

2022

Concurrency and Computation

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This study incorporates artificial neural network (ANN) modeling to design an intelligent road signboard that uses internet of things (IoT) technology to assign the speed limit on interurban highways. The appropriate speed limit must be determined by traffic police experts based on weather conditions and times of the day. Here, an intelligent IoT-based signboard is proposed to announce speed limits on roadways considering some effective parameters, such as temperature, humidity, time, and light. The signboard receives environmental data through its sensors and uses artificial neural networks to compute the speed limit. A feed-forward neural network (FFNN) is provided as the most reliable model. A hybrid training method based on gray wolf optimization and Bayesian regularization is also developed to enhance model performance. The proposed hybrid model converges with an error of less than 3.0% to expert opinion.The model equations are extracted for use in a microcontroller that calculates a safe speed limit based on weather conditions. Additionally, the underlying IoT technology has enabled the police station to remotely monitor and control the developed system. Experimental results demonstrate the reliability of the designed signboard. In all experimental cases, the computed speed limits were in coincidence with the expert's estimations. K E Y W O R D Sartificial neural networks, intelligent signboard, interurban ways, IoT technology, road traffic accidents, speed limit INTRODUCTIONSurprisingly prevalent on a global scale, road traffic accidents constitute one of the leading causes of human mortality and chronic injury. A fatal traffic collision occurs every 50 s, while road injuries occur every 2 s on average. 1,2 Traffic accident casualties account for 30% to 70% of orthopedic beds in hospitals in developing countries. According to the World Health Organization (WHO), nearly 1.3 million people die annually on the world's roadways, while 20-50 million are affected by severe traumas. Unless appropriate measures are taken, the WHO predicts that traffic accidents will be the seventh leading cause of death by 2030. 3 The majority of victims require expensive, long-term care. Road accidents waste 1%-3% of the gross domestic product in many countries, resulting in substantial economic losses irrespective of the grief and suffering. 4 As a result, one of the most critical concerns in all countries is preventing the repercussions of traffic accidents.Distracted driving, exceeding the speed limit, drunk driving, drug use, rain, snow, car defects, teenage drivers, tire blowouts, and night driving are the most common causes of car accidents. 5,6 Several causes of road accidents may be effectively prevented; however, this requires continuous efforts to develop new methods and programs that can improve road traffic safety.

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“…ey surpass other techniques in terms of flexibility on the dataset and free assumption in the procedure. ere is no need for advanced statistical analysis prior to modeling [34][35][36]. By the use of ANN models, the patterns in the specification of problem and effective factors in an accident can be identified.…”

Section: Ann Approachmentioning

confidence: 99%

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Habibzadeh

Ameri

Haghighi

et al. 2022

Mathematical Problems in Engineering

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Traffic accidents occur due to a combination of factors that lead to casualties and injuries. By identifying the most effective factors, it is possible for safety authorities to provide appropriate solutions for decreasing the accident severity and implementing preventive measures. The aim of this research was to present models to predict the accident severity on two-lane two-way (TLTW) rural highways in Iran over a one-year period from 2019 to 2020. Therefore, the occurrence probability of any type of accident was determined by artificial neural network (ANN)-based prediction models using nine independent variables affecting the accident severity. This study developed numerous ANN structures using back-propagation to model the potential nonlinear relationship between the accident severity and accident-related factors. Results indicated that among the models, the multilayer perceptron neural network (MLPNN) model with 6-2-2 partition had the best performance and prediction power. This model was developed using the standardized rescaling method for covariates and batch for training. Also, 9, 5, and 2 units were considered automatically for the input, hidden, and output layers, respectively, and the hyperbolic tangent and softmax were used as an activation function in the hidden and output layers, respectively. This model had the lowest cross-entropy error of 39.6 and the highest correct percentage of 82.5%, and the area under the receiver operating characteristic (ROC) curve was 0.852. Moreover, among all the effective variables, pavement condition index, roadside hazard, shoulder width, and passing zone ratio had the greatest impact on the accident severity. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.

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Analysis and prediction of double-carriage train wheel wear based on SIMPACK and neural networks

Cited by 20 publications

References 27 publications

Application of Capital Asset Pricing Model Based on BP Neural Network in E-commerce Financing

Application of Capital Asset Pricing Model Based on BP Neural Network in E-commerce Financing

An intelligent signboard to assign the speed limit on ways using ANN

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

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