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Speed prediction models for car and sports utility vehicleat locations along four-lane median divided horizontal curves

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Journal of Modern Transportation 2018年第4期26卷 278-284页

作者： Avijit Maji ayush tyagi Department of Civil Engineering Indian Institute of Technology Bombay Mumbai India RITES Ltd. Vashi Navi Mumbai India

Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(MC), point of tangent(PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.

关键词： Vehicle speed prediction model Four-lane median divided highway Horizontal curve Regression analysis The 85th percentile speed

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Deep Learning-Based Speech Emotion Recognition: Leveraging Diverse Datasets and Augmentation Techniques for Robust Modeling

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Journal of Harbin Institute of Technology(New series) 2024年

作者： ayush Porwal Praveen Kumar tyagi Ajay Sharma Dheeraj Kumar Agarwal Department of Electronics and Instrumentation Engineering， Shri G.S.Institute of Technology and Science Department of Electronics and Communication Engineering， Maulana Azad National Institute of Technology School of Computing Science and Engineering， VIT Bhopal University

In recent years， Speech Emotion Recognition (SER) has developed into an essential instrument for interpreting human emotions from auditory data. The proposed research focused on the development of a SER system employing deep learning and multiple datasets containing samples of emotive speech. The primary objective of this research endeavor is to investigate the utilization of Convolutional Neural Networks (CNNs) in the process of sound feature extraction. Stretching， pitch manipulation， and noise injection are a few of the techniques utilized in this study to improve the data quality. The investigation includes coverage of these methods. Feature extraction methods including Zero Crossing Rate， Chroma＿stft， MFCC， RMS， and MelSpectogram are used to train a model. By using these techniques， audio signals can be transformed into recognized features that can be utilized to train the model. Ultimately， the study will produce a thorough evaluation of the model’s performance. When this method was applied， the model achieved an impressive accuracy of 94.57% on the test dataset. Proposed work also validated on EMO-BD and IEMOCAP dataset. These consist of further data augmentation， feature engineering， and hyperparameter optimization. By following these development paths， SER systems will be able to be implemented in real-world scenarios with greater accuracy and resilience.

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