Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Artificial neural networks (ANN)

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    Prediction and optimization of dimensional shrinkage variations in injection molded parts using forward and reverse mapping of artificial neural networks
    (2012) Gowdru Chandrashekarappa, G.C.; Krishna, P.
    The most significant process parameters affecting dimensional shrinkage in transverse and longitudinal directions of molded parts in Plastic Injection Molding (PIM) process are injection velocity, mold temperature, melt temperature and packing pressure. In the present work, ANN model was developed for forward and reverse mapping prediction. In forward mapping PIM process parameters are expressed as the input parameters to predict dimensional shrinkage, whereas in reverse mapping, attempts were made to predict an appropriate set of process parameters required for arriving at the required dimensional shrinkage. The trained network with one thousand input-output data randomly generated from regression equations reported by earlier researchers resulted in minimum mean squared error. The performance of developed model was compared with experimental values for ten different test cases. The results show that ANN model with both forward and reverse mapping is capable of prediction with an error level of less than ten percent. © (2012) Trans Tech Publications.
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    Machine Learning-Based Gap Acceptance Model for Uncontrolled Intersections Under Mixed Traffic Conditions
    (Springer Science and Business Media Deutschland GmbH, 2023) Arathi, A.R.; Harikrishna, M.; Mohan, M.
    Uncontrolled intersections are the most common type of intersections in a transportation network. The study modeled minor road driver’s decision of accepting or rejecting a gap at four-legged uncontrolled intersections having similar geometric characteristics using Artificial Neural Network (ANN) model, Logistic Regression (LR) model, and Support Vector Machine (SVM) model. The results reveal that the performance of LR and SVM models are somewhat similar, while the performance of ANN model exceeds the performance of both LR and SVM models with a correct prediction of about 96.2%. Also, the higher values of the goodness of fit measures like F1 score and R2 value together with a lower value of MSE show that ANN model is better in distinguishing between the classes. The variable gap duration has a major influence on model prediction comparing to other variables. The effect of the critical gap, occupancy time, conflicting volume, and vehicle type are also found remarkable. © 2023, Transportation Research Group of India.