VINOTH KUMAR R

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PTIT

Ahmed Farouk AbdelGawad

Zagazig University

Balamanikanda Suthan

nigatu belay

ogbonna ifeanyichukwu

anikethan venugopal

Coventry, UK at MSRSAS B"lore

Mohd Shukry Abdullah

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Papers by VINOTH KUMAR R

Aerodynamic Design Improvement for an Intercity Bus

Intercity buses travel about 250 to 350 km in a stretch and usually are of sleeper coach mode. Th... more Intercity buses travel about 250 to 350 km in a stretch and usually are of sleeper coach mode. The exterior styling, sleeper comfort and aerodynamically efficient design for reduced fuel consumption are the three essential factors for a successful operation in the competitive world. The bus body building companies prioritizes the exterior looks of the bus and ignore the aerodynamic aspect. Scientific design of sleepers for increased comfort of the passengers is seldom seen. The overall aim of this project was to redesign an intercity bus with enhanced exterior styling, reduced aerodynamic drag and increased comfort for the passengers. Principles of product design were used to analyze the styling and comfort. The benchmarked high floor bus was redesigned with low - floor for reduced aerodynamic drag. The exterior was redesigned with emphasis on improvised aerodynamic performance and appealing looks. The interior was modified to meet aspirations of the commuters. The results of the re...

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Model-Based Control of a BCF Mode Carangiform Bioinspired Robotic Fish

Marine Technology Society Journal, 2014

Bioinspired robotic locomotion in the ocean environment can unveil critical issues on maneuverabi... more Bioinspired robotic locomotion in the ocean environment can unveil critical issues on maneuverability, efficiency, and power consumption. This paper describes the modeling and closed-loop control of a bioinspired robotic fish. A body-caudal fin (BCF) carangiform swimming mode is presented. The propulsion scheme simulates the oscillatory motion of fish tail as thrust generator. The manufactured prototype is a 45-cm-long BCF mode four-joint, 6 degree of freedom modular robotic fish with a horizontal caudal fin (tail). The system uses DC servomotors as actuators and is controlled by microcontroller dsPIC33F. The mechanical CAD design in done in Solidworks and its 3D motion simulations in Matlab VRML, respectively. Lagrange-based dynamic modeling is done for the robotic fish. Based on the model, two nonlinear closed-loop control schemes, namely computed torque method and feed-forward control, both with dynamic PD compensation, are evaluated. This paper compares these model-based control...

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Design, modeling and open-loop control of a BCF mode bio-mimetic robotic fish

2011 International Siberian Conference on Control and Communications (SIBCON), 2011

AbstractThis paper deals with the design of a bio-inspired fish like underwater vehicle. As the ... more

Application of deep learning methods for urban water demand forecast modelling

<p>Water demand predictions forms an integral part of sustainable management practices for ... more <p>Water demand predictions forms an integral part of sustainable management practices for water supply systems. Demand prediction models aides in water system maintenance, expansions, daily operational planning and in the development of an efficient decision support system based on predictive analytics. In recent years, it has also found wide application in real-time control and operation of water systems as well. However, short term water demand forecasting is a challenging problem owing to the frequent variations present in the urban water demand patterns. There are numerous methods available in literature that deals with water demand forecasting. These methods can be roughly classified into statistical and machine learning methods. The application of deep learning methods for forecasting water demands is an upcoming research area that has found immense traction due to its ability to provide accurate and scalable models. But there are only a few works which compare and review these methods when applied to a water demand dataset. Hence, the main objective of this work is the application of different commonly used deep learning methods for development of a short-term water demand forecast model for a real-world dataset. The algorithms studied in this work are (i) Multi-Layer Perceptron (MLP) (ii) Gated Recurrent Unit (GRU) (iii) Long Short-Term Memory (LSTM) (iv) Convolutional Neural Networks (CNN) and (v) the hybrid algorithm CNN-LSTM. Optimal supervised learning framework required for forecasting the one day ahead water demand for the study area is also identified. The dataset used in this study is from Hillsborough County, Florida, US. The water demand data was available for a duration of 10 months and the data frequency is about once per hour. These algorithms were evaluated based on the (1) Mean Absolute Percentage Error (MAPE) and (ii) Root Mean Squared Error (RMSE) values. Visual comparison of the predicted and true demand plots was also employed to check the prediction accuracy. It was observed that, the RMSE and MAPE values were minimal for the supervised learning framework that used the previous 24-hour data as input. Also, with respect to the forecast accuracy, CNN-LSTM performed better than the other methods for demand forecast, followed by MLP. MAPE values for the developed deep learning models ranged from 5% to 25%. The quantity, frequency and quality of data was also found to have substantial impact on the accuracy of the forecast models developed. In the CNN-LSTM based forecast model, the CNN component was found to effectively extract the inherent characteristics of historical water consumption data such as the trend and seasonality, while the LSTM part was able to reflect on the long-term historical process and future trend. Thus, its water demand prediction accuracy was improved compared to the other methods such as GRU, MLP, CNN and LSTM.</p>

Aerodynamic Design Improvement for an Intercity Bus

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Model-Based Control of a BCF Mode Carangiform Bioinspired Robotic Fish

Marine Technology Society Journal, 2014

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Design, modeling and open-loop control of a BCF mode bio-mimetic robotic fish

2011 International Siberian Conference on Control and Communications (SIBCON), 2011

AbstractThis paper deals with the design of a bio-inspired fish like underwater vehicle. As the ... more

Application of deep learning methods for urban water demand forecast modelling

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