Dr. Praveen Sankarasubramanian
@rmdresearchlabs.co.in
Independent Researcher and
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Scopus Publications
Praveen Sankarasubramanian
IEEE
Extreme precautions must be observed to handle toxic wastes, radioactive substances, chemical raw materials, chemical wastes, and bio-products in different industries. Any malfunction in a dangerous traffic network can lead to serious accidents, deaths and / or serious damage. Direct monitoring and analysis, and preventive measures to prevent the spread of failures, can significantly reduce the recurrence of adverse effects. Current research suggests that detailed publicity and information on the latest developments in pipeline monitoring and research may help modernize the oil industry in the future. We also propose a framework to detect timely leakage in pipelines, especially in oil and gas sector.
Praveen Sankarasubramanian and E. Ganesh
Liquid metals are commonly used in chemical industries and nuclear reactors. Since liquid metals may be hazardous, they should be handled very carefully. Careless handling might cause an adverse effect and even disasters. Corrosion and pressure can deteriorate the structure that handles the liquid metals. Leakage of liquid metals can result in ecological disasters and can lead to a humanitarian crisis. Early warning systems, detection of the accident, and prompt steps taken after the incident are the three important phases of monitoring. Continuous monitoring and timely detection of risk reduce the impact caused by the leakage of liquid metal. At present, industries have sensors-based detection. This paper proposes an enhanced version of the existing system. Here, continuous monitoring uses sensors, the Internet of things (IoT), and an artificial intelligence-based system. In this paper, the conventional system is integrated with AI to identify indoor and open-air fire situations. This paper discusses different data collected and investigated data from the videos, sensors, other monitoring systems. And the false-positive results are reduced by using the proposed methodology.
Vaishnav Kumar Suresh Kumar and Praveen Sankarasubramanian
Fire disasters due to pipeline leaks result in loss to life and property. Therefore, an expeditious model to detect smoke and fire is much needed. Even though there have been several types of research done on fire and smoke detection, most of these focuses on very generic datasets that boast good performance; the case of monitoring pipelines remotely necessitates a focused approach for enhanced performance for early detection. This paper attempts to develop a model specifically for this purpose and can be deployed more confidently in a pipeline environment. We have also customized the existing dataset by adding images of pipelines to bias the dataset and give a more confidence rate while predicting. Our proposed neural network architecture achieves higher accuracy, precision, recall, and F-measure. Also, the lightweight makes it easily deployable on embedded platforms as well. The performance of our model is evaluated against FireNet on our biased dataset, sounds very promising.