Tata Consultancy Services Limited

Sunil D Joshi

Siddhartha Sengupta

Shripad Salsingikar

Balamuralidhar Purushothaman

Jayavardhana Rama Gubbi Lakshminarasimha

Sreehari Kumar Bhogineni

Nishant Kumar Agrawal

Rishin Raj

Mayank Mishra

Vishnu Hariharan Anand

Vishal Bajpai

Mahesh Rangarajan

Gopi Kandaswamy

Vaibhav Vyawahare

Sudhir Kumar Sinha

Kejitan Dontas

Jegan Mohan Ponraj

Srinivas Kotamraju

This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.

Systems and methods for data acquisition and asset inspection in presence of magnetic interference

Disclosed is method and system for continuously re-generating reactive on-line train schedules for trains running in a large railway network. Railway network partitioned based on user configuration, into first type comprising trunk line and feeder line sub-networks, and second type comprising supervisory dispatch control territories. Sense and respond cycle is continuously executed on multi-processor computing environment, senses dynamic data from field about train movements, and other changes from users. For each first type sub-network, degree of deviation is computed from incumbent plans and congestion in sub-networks. Using degree of deviation and congestion, trains are rerouted and suitable scheduling methods are chosen for each sub-network and executed in parallel and first level train schedules are sent to second level train schedulers working on second type sub-networks which in parallel identify and resolve conflicts among first level train schedules. Second level train schedules are collated to generate reactive on-line network train schedule.

System and method for generating vehicle movement plans in a large railway network

The present disclosure relates to a system and a method for vehicle movement modeling in a network. The modeling may be characterized by vehicle related intelligence gathering, processing and dissemination thereof for an adaptive rescheduling of the vehicle movement in the railway network. Predefined data associated with the vehicle in the railway network is acquired and is further processed to resolve one or more conflicts associated with the vehicle movement. The processing may include allocating resources, developing plans for voyages, and continuously gathering deviation data. The vehicle movement modeling may also include generating detailed layouts of vehicle movements for particular time-periods over the railway network.

System and method for vehicle movement modeling in a railway network

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Maharashtra, India

Sunil D Joshi