Location Selection of Public Transit Transfer Hubs Based on Gray NN Model Improved by GA

2013 ◽  
Vol 660 ◽  
pp. 174-178
Author(s):  
Min An Tang ◽  
Xiao Ming Wang ◽  
Shuang Yuan ◽  
Zhen Rong Sun
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Public Transit ◽  
Small Sample ◽  
Location Selection ◽  
Lanzhou City ◽  
Gray System Theory ◽  
The Neural Network ◽  
Public Traffic ◽  
Neural Network Prediction

The public traffic flow has the gray characteristics of “small sample and poor information”, thereby a forecast method for transfer flow based on the gray soft computing is proposed. This method utilizes the gray system theory to establish gray neural network prediction model, aiming to improve performance of the neural network as well as the accuracy of the system’s prediction by using genetic algorithm. The results show that the optimized model can more accurately predict the traffic flow, providing a more effective way of location selection for public transit transfer hubs. Finally, take the planning of public transit transfer hubs in Lanzhou City as an example to carry out empirical analysis and evaluation for the transfer hubs using this method

Research on the Application of the Prediction of the Expressway Traffic Flow Based on the Neural Network with Genetic Algorithm

2011 ◽  
Vol 189-193 ◽  
pp. 4400-4404 ◽  
Author(s):  
Chun Mei Zhu ◽  
Chang Peng Yan ◽  
Xiao Li Xu ◽  
Guo Xin Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Mixed Model ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Speed Up ◽  
Neural Network Algorithm ◽  
Genetic Neural Network

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

scholarly journals UTILIZING SITE CHARACTERISTICS IN NEURAL NETWORK MODELLING OF PERCENTAGE COST-TIME OVERRUN OF BUILDING PROJECTS

2017 ◽  
Vol 8 (1) ◽  
pp. 1-15
Author(s):  
A. O. Ujene ◽  
A. A. Umoh
Keyword(s):  
Neural Network ◽  
Cost Overrun ◽  
Construction Costs ◽  
Network Modelling ◽  
Building Projects ◽  
The Neural Network ◽  
Site Characteristics ◽  
Neural Network Prediction ◽  
The Cost ◽  
Prediction Approach

This study evaluated the site characteristics influencing the time and cost delivery of building projects, determined the range of percentage cost and time overrun and developed a neural network model for predicting the percentage cost and time overrun using the site characteristics of building projects. The study evaluated twelve site characteristics and two performance indicators obtained from records of construction costs, contract documents, and valuation reports of 126 purposively sampled building projects spread across several cities in Nigeria. Analyses were with descriptive and artificial neural network. It was concluded that with fairly favourable site characteristics, cost overrun range reached 77.95% with a mean variation of 44.36%, while time overrun range reached 51.23% with a mean variation of 26.77%. It was found that the accuracy performance levels of 91.93% and 91.43% for the cost and time overrun predictions respectively were very high for the optimum models. Building projects have eight significant site characteristics which can be used to reliably predict the percentage overrun, among which the ground water level, level of available infrastructure and labour proximity around the site are the most important predictors of cost and time overrun. The study recommended that project owners, consultants, contractors and other stakeholders should always use the eight identified site characteristics in predicting percentage cost and time overrun, with more priority on the first three characteristics. The study also recommended the neural network prediction approach due to its prediction accuracy.

scholarly journals Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1662
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
High Speed ◽  
Prediction Method ◽  
High Speed Train ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

Research of Air Traffic Flow Forecasts Based on BP Neural Network

2013 ◽  
Vol 671-674 ◽  
pp. 2912-2915
Author(s):  
Ming Qiang Chen ◽  
Jun Hong Feng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Back Propagation ◽  
Good Method ◽  
Back Propagation Neural Network ◽  
Air Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
The Neural Network ◽  
Air Traffic Flow

Air traffic is increasing worldwide at a steady annual rate, and airport congestion is already a major issue for air traffic controllers. The traditional method of traffic flow prediction is difficult to adapt to complex air traffic conditions. Due to its self-learning, self-organizing, self-adaptive and anti-jamming capability, the neural network can predict more effectively the air traffic flow than the traditional methods can. A good method for training is an important problem in the prediction of air traffic flow with neural network. This paper will try to find a new model to solve the traffic flow prediction problem by back propagation neural network.

The Neural Network Prediction to the Wear and Tear Fault of Thrust Bearing in Air Compressor

2013 ◽  
Vol 333-335 ◽  
pp. 1758-1761
Author(s):  
Song He Zhang ◽  
Yue Gang Luo ◽  
Bin Wu ◽  
Bing Cheng Wang
Keyword(s):  
Neural Network ◽  
Traditional Method ◽  
Thrust Bearing ◽  
Axial Thrust ◽  
Air Compressor ◽  
The Neural Network ◽  
Wear And Tear ◽  
Neural Network Prediction ◽  
Tear Fault ◽  
Artificial Neural Network Ann

The wear and tear allowances (displacements) of axial thrust bearing in air compressor was diagnosed and predicted, applying the model of artificial neural network (ANN), and compared with the traditional method of diagnosis and prediction. It showed that the results of diagnosis and prediction are more precise than that of traditional method. It can diagnosis and predicts the wear and tear allowances of axial thrust bearing better.

scholarly journals Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies

2021 ◽  
Author(s):  
Andrea Chatrian ◽  
Richard T. Colling ◽  
Lisa Browning ◽  
Nasullah Khalid Alham ◽  
Korsuk Sirinukunwattana ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under The Curve ◽  
Validation Dataset ◽  
Prostate Biopsies ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
Whole Slide Images

AbstractThe use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

scholarly journals Artificial Intelligence for Advance Requesting of Immunohistochemistry in Diagnostically Uncertain Prostate Biopsies

2021 ◽  
Author(s):  
Andrea Chatrian ◽  
Richard Colling ◽  
Lisa Browning ◽  
Nasullah Khalid Alham ◽  
Korsuk Sirinukunwattana ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under The Curve ◽  
Validation Dataset ◽  
Prostate Biopsies ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Neural Network Algorithm ◽  
Network Prediction ◽  
Whole Slide Images

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by 3-fold cross-validation. Validation was conducted on a separate validation dataset of 212 images. Non IHC-requested cases were diagnosed in 17.9 minutes on average, while IHC-requested cases took 33.4 minutes over multiple reporting sessions. We estimated 11 minutes could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.

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