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您的检索式：作者名="Debasish PAL"
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| 1 | Grain-size distribution in suspension over a sand-gravel bed in open channel flow显示文摘Grain-size distributions of suspended load over a sand-gravel bed at two different flow velocities were studied in a laboratory flume.The experiments had been performed to study the influence of flow velocity and suspension height on grain-size distribution in suspension over a sand-gravel bed.The experimental findings show that with an increase of flow velocity,the grain-size distribution of suspended load changed from a skewed form to a bimodal one at higher suspension heights.This study focuses on the determination of the parameter β_n which is the ratio of the sediment diffusion coefficient to the momentum diffusion coefficient of n th grain-size.A new relationship has been proposed involving β_n,the normalizing settling velocity of sediment particles and suspension height,which is applicable for widest range of normalizing settling velocity available in literature so far.A similar parameter β for calculating total suspension concentration is also developed.The classical Rouse equation is modified with β_n and β and used to compute grain-size distribution and total concentration in suspension,respectively.The computed values have shown good agreement with the measured values of experimental data. | Koeli GHOSHAL Debasish PAL | 2014 | International Journal of Sediment Research2014，29，2： | 1 |
| 2 | Support Veetor Regression 显示文摘 | Debasish Basak Sfimanta Pal Dipak Chandra Patranabis | 2007 | Neural Information Processing-Letters and Reviews2007，11，10： | 1 |
| 3 | Support vector regression显示文摘 | Debasish Basak Srimanta Pal Dipak Chandra Patranabis | 2003 | Neural Information Processing-Letters and Reviews2003，11，10： | 1 |
| 4 | Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control显示文摘Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments. | Debasish Mishra Ashok Priyadarshi Sarthak M Das Sristi Shree Abhinav Gupta Surjya K Pal Debashish Chakravarty | 2022 | Journal of Dynamics, Monitoring and Diagnostics2022，1，3： | 0 |
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