NPTEL Introduction To Industry 4.0 And Industrial Internet Of Things Assignment 8 Answers 2023

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NPTEL Introduction To Industry 4.0 And Industrial Internet Of Things Assignment 8 Answers 2023:

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Q.1. Which of the following is not a part of IIoT analytics ?

• (a) Real time data analysis
• (b) Dynamic operations optimization
• (c) Prognostic maintenance
• (d) Renewable energy

Q.2. Prescriptive analytics is one of the categories of IIoT analytics. True or False?

• (a) True
• (b) False

Q.3. Volume, veracity, velocity are the characteristics of which of the following entity?

• (a) SDN
• (b) Image processing
• (c) Big data
• (d) Machine learning

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Q.4. Feature engineering is absent in which of the following tools or algorithms?

• (a) Machine learning
• (b) Deep learning
• (c) Both a and b
• (d) Clustering

Q.5. What is the purpose of diagnostic analytics ?

• (a) Detect irregular behavior of assets quickly in the timescale of milliseconds
• (b) Identify the root cause of an anomaly in the timescale of minutes
• (c) Inform about remaining useful life of an asset
• (d) None of the above

Q.6. How many different categories of clustering are there?

• (a) 2, hard and soft clustering
• (b) 2, long and short clustering
• (c) 2, labeled and unlabelled clustering
• (d) No categories

Q.7. Fuzzy c-Means Clustering can be used when –

• (a) Data points may belong to more than one clusters
• (b) Faster clustering is required
• (c) Both a and b
• (d) Data points strictly belong to only one cluster

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Q.8. Which of the following statements is/are true about linear regression?

• (a) It is an unsupervised learning algorithm
• (b) It is used to label the data
• (c) It is use for prediction of values
• (d) All of the above

Q.9. Select the machine learning-base IIoT application(s) used in the retail sector?

• (a) Drug discovery
• (b) Product recommendation
• (c) Dynamic price setup
• (d) All of the above

Q.10. Select the correct statement.

• (a) Artificial intelligence is a subset of machine learning
• (b) Machine learning is a subset of deep learning
• (c) Machine learning is a subset of artificial intelligence
• (d) Artificial intelligence is a subset of deep learning

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Q.11. What does deep in deep learning refer to?

• (a) Number of visible neurons
• (b) Number of hidden layers
• (c) Number of deep neurons
• (d) Number of deep layers

Q.12. What is/are the critical requirement(s) of deep learning applications?

• (a) Large amount of data.
• (b) High quality data.
• (c) Both a and b.
• (d) Small amount of data

Q.13. Which of the following is/are example(s) of Software-as-a-Service?

• (a) MindSphere
• (b) Microsoft Azure
• (c) DropBox
• (d) All of the above

Q.14. What is/are the characteristic(s) of Good SLA?

• (a) Achievable
• (b) Meaningful
• (c) Affordable
• (d) All of the above

Q.15. Which of the following is/are the limitation(s) of cloud-based approaches in IIoT?

• (a) Unlimited storage
• (b) Interoperability
• (c) Cost efficient
• (d) Higher latency

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About NPTEL Introduction To Industry 4.0 And Industrial Internet Of Things Course:

Industry 4.0 concerns the transformation of industrial processes through the integration of modern technologies such as sensors, communication, and computational processing. Technologies such as Cyber Physical Systems (CPS), Internet of Things (IoT), Cloud Computing, Machine Learning, and Data Analytics are considered to be the different drivers necessary for the transformation. Industrial Internet of Things (IIoT) is an application of IoT in industries to modify the various existing industrial systems. IIoT links the automation system with enterprise, planning and product lifecycle.

Course Layout:

• Week 1  :  Introduction: Sensing & actuation, Communication-Part I, Part II, Networking-Part I, Part II
• Week 2  :  Industry 4.0: Globalization and Emerging Issues, The Fourth Revolution, LEAN Production Systems, Smart and Connected Business Perspective, Smart Factories
• Week 3  :  Industry 4.0: Cyber Physical Systems and Next Generation Sensors, Collaborative Platform and Product Lifecycle Management, Augmented Reality and Virtual Reality, Artifical Intelligence, Big Data and Advanced Analysis
• Week 4  :  Cybersecurity in Industry 4.0, Basics of Industrial IoT: Industrial Processes-Part I, Part II, Industrial Sensing & Actuation, Industrial Internet Systems.
• Week 5  :  IIoT-Introduction, Industrial IoT: Business Model and Referece Architerture: IIoT-Business Models-Part I, Part II, IIoT Reference Architecture-Part I, Part II.
• Week 6  :  Industrial IoT- Layers: IIoT Sensing-Part I, Part II, IIoT Processing-Part I, Part II, IIoT Communication-Part I.
• Week 7  :  Industrial IoT- Layers: IIoT Communication-Part II, Part III, IIoT Networking-Part I, Part II, Part III.
• Week 8  :  Industrial IoT: Big Data Analytics and Software Defined Networks: IIoT Analytics – Introduction, Machine Learning and Data Science – Part I, Part II, R and Julia Programming, Data Management with Hadoop.
• Week 9  :  Industrial IoT: Big Data Analytics and Software Defined Networks: SDN in IIoT-Part I, Part II, Data Center Networks, Industrial IoT: Security and Fog Computing: Cloud Computing in IIoT-Part I, Part II.
• Week 10 : Industrial IoT: Security and Fog Computing – Fog Computing in IIoT, Security in IIoT-Part I, Part II, Industrial IoT- Application Domains: Factories and Assembly Line, Food Industry. 
• Week 11 : Industrial IoT- Application Domains: Healthcare, Power Plants, Inventory Management & Quality Control, Plant Safety and Security (Including AR and VR safety applications), Facility Management.
• Week 12  :  Industrial IoT- Application Domains: Oil, chemical and pharmaceutical industry, Applications of UAVs in Industries, Real case studies :
  Case study – I : Milk Processing and Packaging Industries
  Case study – II: Manufacturing Industries – Part I
  Case study – III : Manufacturing Industries – Part II
  Case study – IV : Student Projects – Part I
  Case study – V : Student Projects – Part II
  Case study – VI : Virtual Reality Lab
  Case study – VII : Steel Technology

CRITERIA TO GET A CERTIFICATE:

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

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