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Mine Automation and Data Analytics
Online
12 Weeks
Free Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 1000 | yes | IIT ISM Dhanbad |
The Syllabus
- Lecture 1 : Introduction to Automation, Principles and Strategies of Automation and enhancement of Productivity
- Lecture 2 : Essential Elements of an Automated System
- Lecture 3 : Autonomous Mining System I: Autonomous Haulage Systems
- Lecture 4 : Autonomous Mining System II: Automated Drilling System
- Lecture 5 : Autonomous Mining System III: Fleet Management System: TDS
- Lecture 6 : Computerised Maintenance Management System, ERP for Mining Industry
- Lecture 7 : Mine Robotics: Mining Remote Operations & Control
- Lecture 8 : Proximity Sensors and Control Systems
- Lecture 9 : Radar Systems, RFID in Mining Engineering
- Lecture 10: Geo-fencing, CCD camera in Mining for safety and management
- Lecture 11: Global Navigational Satellite System in Mining production planning and efficient control of the machine
- Lecture 12: Automated Communication and Tracking Technologies: Image Processing
- Lecture 13: Automated Communication and Tracking Technologies: SCADA
- Lecture 14: Virtual Reality Applications: Mining Equipment Concept development, Mine Safety Applications, Mining operation simulations – Part 1
- Lecture 15: Virtual Reality Applications: Mining Equipment Concept development, Mine Safety Applications, Mining operation simulations – Part 2
- Lecture 16: Virtual Reality Applications: Mining Equipment Concept development, Mine Safety Applications, Mining operation simulations – Part 3
- Lecture 17: Descriptive Statistics: Introduction
- Lecture 18: Probability Distributions and Inferential Statistics: Hypothesis tests – Part 1
- Lecture 19: Probability Distributions and Inferential Statistics: Hypothesis tests – Part 2
- Lecture 20: Probability Distributions and Inferential Statistics: Hypothesis tests – Part 3
- Lecture 21: Probability Distributions and Inferential Statistics: Hypothesis tests – Part 4
- Lecture 22: Regression & ANOVA
- Lecture 23: Machine Learning: Introduction
- Lecture 24: Perceptron: Linear Classifier
- Lecture 25: Support Vector Machine
- Lecture 26: Concepts of Supervised Learning: Neural Networks, Deep learning
- Lecture 27: Unsupervised Learning and Challenges for Big Data Analytics: Clustering
- Lecture 28: Application of Big Data Analytics and Artificial Intelligence (AI) in Mining
- Lecture 29: Case studies on Cognitive Maintenance of Mining Systems
- Lecture 30: Case studies on Orebody modelling and Mine Design etc.