Estimating Uncertainty of Work Zone Capacity using Neural Network Models

2019 ◽  
Vol 2673 (2) ◽  
pp. 49-59
Author(s):  
Zilin Bian ◽  
Kaan Ozbay
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Prediction Intervals ◽  
Work Zone ◽  
Estimation Methods ◽  
Work Intensity ◽  
Neural Network Models ◽  
The Neural Network

This study aims to develop a neural network model to predict work zone capacity including various uncertainties stemming from traffic and operational conditions. The neural network model is formulated in terms of the number of total lanes, number of open lanes, heavy vehicle percentage, work intensity, and work duration. The data used in this paper are obtained from previous studies published in open literature. To capture the uncertainty of work zone capacity, this paper provides two recent methods that enable neural network models to generate prediction intervals which are determined by mean work zone capacity and prediction standard error. The research first builds a Bayesian neural network model with the application of black-box variational inference (BBVI) technique. The second model is based on a regular artificial neural network with an application of the recently proposed Monte-Carlo dropout technique. Both of the neural network models construct prediction intervals under various confidence levels and provide the coverage rates of the actual work zone capacities. The statistical accuracy (MAPE, MAE, MSE, and RMSE) of the models is then compared with traditional estimation methods in predicted mean work zone capacity. BBVI produces better statistical results than the other three models. Both of the models provide predicted work zone capacity distribution and prediction intervals, whereas traditional models only provide a single estimate.

An explicit methodology for manufacturing cost–tolerance modeling and optimization using the neural network integrated with the genetic algorithm

2020 ◽  
Vol 34 (3) ◽  
pp. 430-443
Author(s):  
A. Saravanan ◽  
J. Jerald ◽  
A. Delphin Carolina Rani
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Manufacturing Cost ◽  
Genetic Operators ◽  
Neural Network Models ◽  
Data Set ◽  
The Neural Network

AbstractThe objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.

scholarly journals Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

2020 ◽  
Author(s):  
Wen-Hsien Chang ◽  
Han-Kuei Wu ◽  
Lun-chien Lo ◽  
William W. L. Hsiao ◽  
Hsueh-Ting Chu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Graphics Processing Units ◽  
Network Models ◽  
Neural Network Models ◽  
The Neural Network ◽  
Objective Evidence

Abstract Background: Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located.Methods: A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results: Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions.Conclusions: This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

scholarly journals Summarize the Work Related to Prediction of Stock Market’s Option Prices with Artificial Intelligence using Standard Dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 5023-5031
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Rbf Neural Network ◽  
Network Models ◽  
Energy Potential ◽  
Neural Network Models ◽  
The Neural Network ◽  
Bias Function

Forecasting and prediction are based on pattern recognition. It may be a human energy potential increase day today when he grownup a young guy, but afterward, his energy potential going downwards. So, we observed the pattern with the help of neural network models; these are radical bias function (RBP) and back-propagation (BP). Utilizing the neural network model, it also has many classification parts like a deep neural network, feedforward neural network, recurrent neural network, convolutional neural network and many more. In the forecasting or prediction, we have a large amount of data to manage. We trained the data with algorithm and here we also use the neural network models. We used optimization techniques that are inspired by biological swarm. Nowadays, lots of data generate day by day like market, medical, education, automobile, etc. we need recognition of the pattern for prediction of future expectations. That expectation of prediction very helpful and needy to gain profit of human beings. In this work, we use SOM (self-Organized Map), RBF (Radical Bias Function), DNN (Deep Neural Network) and PGO (Plant Grow Optimization). The total data point for the processing used 27500. The evaluation of the performance used standard parameters such as ET, MAE, MSE, RMSE and MI. The proposed algorithm implemented in MATLAB software. The cascaded neural network classifier is the combination of the SOM and RBF neural network models. The SOM neural network model proceeds the task of clustering and RBF neural network model used for prediction.

scholarly journals Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

2020 ◽  
Author(s):  
Wen-Hsien Chang ◽  
Han-Kuei Wu ◽  
Lun-chien Lo ◽  
William W. L. Hsiao ◽  
Hsueh-Ting Chu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Graphics Processing Units ◽  
Network Models ◽  
Neural Network Models ◽  
The Neural Network ◽  
Objective Evidence

Abstract Background Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located. Methods A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions. Conclusions This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

Understanding and predicting the quality determinants of e-government services

2015 ◽  
Vol 10 (3) ◽  
pp. 325-340 ◽  
Author(s):  
Sujeet Kumar Sharma ◽  
Srikrishna Madhumohan Govindaluri ◽  
Said Gattoufi
Keyword(s):  
Neural Network ◽  
Service Quality ◽  
Network Model ◽  
Neural Network Model ◽  
Network Models ◽  
Demographic Variables ◽  
Neural Network Models ◽  
Content Type ◽  
Government Services ◽  
The Neural Network

Purpose – The purpose of this paper is to investigate the quality determinants influencing the adoption of e-government services in Oman and compare the performance of multiple regression and neural network models in identifying the significant factors influencing adoption in Oman. Design/methodology/approach – Primary data concerning service quality determinants and demographic variables were collected using a structured questionnaire survey. The variables selected in the design of the questionnaire were based on an extensive literature review. Factor analysis, multiple linear regression and neural network models were employed to analyze data. Findings – The study found that quality determinants: responsiveness, security, efficiency and reliability are statistically significant predictors of adoption. The neural network model performed better than the regression model in the prediction of e-government services’ adoption and was able to characterize the non-linear relationship of the aforementioned predictors with the adoption of e-government services. Further, the neural network model was able to identify demographic variables as significant predictors. Practical implications – This study highlights the importance of service quality in the adoption of e-government services and suggests that an enhanced focus and investment on improving quality of the design and delivery of e-government services can have a positive impact on the usage of the services, thereby enabling the Oman Government in achieving the governance objectives for which these technologies were employed. Originality/value – Studies in the area of e-government typically focus either on technology adoption problems or service quality problems. The role of service quality in adoption is rarely addressed. The research presented in this paper is of great value to the institutions that are involved in the development of technology-based e-government services in Oman.

scholarly journals Performance analysis of the TensorFlow library with different optimisation algorithms

2021 ◽  
Vol 21 ◽  
pp. 330-335
Author(s):  
Maciej Wadas ◽  
Jakub Smołka
Keyword(s):  
Neural Network ◽  
Performance Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Training Time ◽  
The Neural Network ◽  
Training Efficiency

This paper presents the results of performance analysis of the Tensorflow library used in machine learning and deep neural networks. The analysis focuses on comparing the parameters obtained when training the neural network model for optimization algorithms: Adam, Nadam, AdaMax, AdaDelta, AdaGrad. Special attention has been paid to the differences between the training efficiency on tasks using microprocessor and graphics card. For the study, neural network models were created in order to recognise Polish handwritten characters. The results obtained showed that the most efficient algorithm is AdaMax, while the computer component used during the research only affects the training time of the neural network model used.

scholarly journals Tongue fissure visualization by using deep learning – an example of the application of artificial intelligence in traditional medicine

2019 ◽  
Author(s):  
Wen-Hsien Chang ◽  
Han-Kuei Wu ◽  
Lun-chien Lo ◽  
William W. L. Hsiao ◽  
Hsueh-Ting Chu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Graphics Processing Units ◽  
Network Models ◽  
Neural Network Models ◽  
The Neural Network ◽  
Objective Evidence

Abstract Background Traditional Chinese medicine (TCM) describes physiological and pathological changes inside and outside the human body by the application of four methods of diagnosis. One of the four methods, tongue diagnosis, is widely used by TCM physicians, since it allows direct observations that prevent discrepancies in the patient’s history and, as such, provides clinically important, objective evidence. The clinical significance of tongue features has been explored in both TCM and modern medicine. However, TCM physicians may have different interpretations of the features displayed by the same tongue, and therefore intra- and inter-observer agreements are relatively low. If an automated interpretation system could be developed, more consistent results could be obtained, and learning could also be more efficient. This study will apply a recently developed deep learning method to the classification of tongue features, and indicate the regions where the features are located. Methods A large number of tongue photographs with labeled fissures were used. Transfer learning was conducted using the ImageNet-pretrained ResNet50 model to determine whether tongue fissures were identified on a tongue photograph. Often, the neural network model lacks interpretability, and users cannot understand how the model determines the presence of tongue fissures. Therefore, Gradient-weighted Class Activation Mapping (Grad-CAM) was also applied to directly mark the tongue features on the tongue image. Results Only 6 epochs were trained in this study and no graphics processing units (GPUs) were used. It took less than 4 minutes for each epoch to be trained. The correct rate for the test set was approximately 70%. After the model training was completed, Grad-CAM was applied to localize tongue fissures in each image. The neural network model not only determined whether tongue fissures existed, but also allowed users to learn about the tongue fissure regions. Conclusions This study demonstrated how to apply transfer learning using the ImageNet-pretrained ResNet50 model for the identification and localization of tongue fissures and regions. The neural network model built in this study provided interpretability and intuitiveness, (often lacking in general neural network models), and improved the feasibility for clinical application.

scholarly journals Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

2021 ◽  
Vol 13 (23) ◽  
pp. 4801
Author(s):  
Hanlin Chen ◽  
Fei Niu ◽  
Xing Su ◽  
Tao Geng ◽  
Zhimin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Orbit Error ◽  
Broadcast Ephemeris ◽  
Bp Neural Network Model ◽  
Orbit Errors

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

scholarly journals The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Liwen Zhang ◽  
Chao Zhang ◽  
Zhuo Sun ◽  
You Dong ◽  
Pu Wei
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Long Span ◽  
Long Span Bridge ◽  
Vibration Responses ◽  
Bp Neural Network Model

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

Modified BP Neural Network Model is Used for Odd-Even Discrimination of Integer Number

2013 ◽  
Vol 423-426 ◽  
pp. 2675-2678 ◽  
Author(s):  
Bao Long Hu ◽  
Ji Ren Xu ◽  
Huai Hui Gao ◽  
Ji Hai Liu ◽  
Ke Ren Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Bp Algorithm ◽  
Integer Number ◽  
Neural Network Models ◽  
Bp Neural Network Model ◽  
Bp Model

This paper introduced the BP neural network model and the BP algorithm in detail, and pointed out the BP neural network existed the defects of local optimal tendency of local optimal, slowed convergence speed etc. Through the modified BP algorithm, we could solve the problems existing in the traditional BP algorithm successfully, simulation results for odd-even discrimination of integer number based on MATLAB BP algorithm show that modified BP model compared with BP model has faster training speed and high study accuracy. Modified BP neural network models is used in practice, as long as it is complementary with effective measures, and we can get satisfactory result completely.

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