AUGMENTED REALITY AND MACHINE LEARNING SUPPORT FUNCTIONS

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Augmented Reality detects as a marker and triggers on top of the real world image displaying on the screen. Handheld devices such as smart-phones and iPad are the other ways to use for augmented reality. Augmented Reality resides the object “augmented” across multiple sensory modalities which also relates into two terms: mixed reality and computer-mediated reality. AR functional requirements make AR projects successful which is influenced by the development of road-maps and product launches.

 

First Augmented Reality:

The Sensorama is the first augmented reality in the form of Interactive theatre experience in 1957 but not patented until 1962.

AR Hardware Functional Requirements
The following are the Hardware Functional Requirements of Augmented Reality.

1. Battery Life

2. Connectivity

3. Field of View

4. On-board Storage

5. On-board OS

6. Environmental

7. Input / Output

8. Eye Tracking

9. GPS

10. Mouse / Touch Pad

11. Microphone

12. Sound

13. Display

14. Safety

15. Visual Tracking

16. Wear Ability / Comfort

AR Software Functional Requirements:
The following are the software functional requirements of Augmented Reality

1. Adding API Links

2. Ease of Use

3. Output to Branded Application

4. Display PDFs

5. Display HTML

6. Display Slide Show

7. Display MP4

8. Creating 3D content

9. Deployment of Augmented Reality Content

10. Content Storage

11. Visual Tracking Method

12. Zoom

13. Geometry Rotation

14. Snapshots

15. Record Video

16. Remote Guidance

17. Workflow Authoring

18. Get Files

19. Alert Me

20. Quote Me

21. Security

22. Internet of Things

Machine Learning (ML):

Machine Learning comes under Augmented Reality which uses statistical techniques with the ability to learn data. Machine Learning also relates to computational statistics focusing more on prediction-making through use of computers. Machine Learning also supervises learning and establishing behavioral profiles with meaningful anomalies. Since then, humans do not use machine language because it is impossible for humans to use as they consist entire numbers and programmers use a high-level programming language or an assembly language.

These programs had been written in the high-level language is translated into assembly language or machine language by a compiler and the assembly language translated into machine language is called an assembler.

The following are the functional requirements defined for machine learning:
1. Interoperability and Open Architecture
2. Asset and Sensor Neutrality
3. Alert Generation
4. Machine Learning Methodology
5. Asset Visualization

Interoperable Open Architecture:
Interoperable Open Architecture delivers interoperability with subsystems and applications built and procured at different times.

Asset and Sensor Neutrality:
This kind of proposition assumes an equal opportunity of making a profit as the market does not discriminate the status of agents and cares only about profitability as the function of the price determined as PCFM (price control financial model). The main consideration of the solution functions heterogeneous plant environments with data from all production assets and also the solution is tied to one class of sensors or processes.

Alert Generation:
If there is any machine degradation detected, this is communicated to the relevant facility stakeholders.

Machine Learning Methodology:

This methodology plays a vital role in the interpretation of generated data assets and the kind of Predictive Asset Maintenance is based on Big Data methodology.

Asset Visualization:
There are no technicians accessing at a facility level and will not be trained in Artificial Intelligence and Big Data. With key considerations defining the requirement are the visualization of machine behavior with the ability to depict health of machinery and takes specific action as a result.

Conclusion:

This concludes the supportive functions of Augmented Reality and Machine Learning for both Hardware and Software.

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