Using MIS (9th Edition)
9th Edition
ISBN: 9780134106786
Author: David M. Kroenke, Randall J. Boyle
Publisher: PEARSON
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Expert Solution & Answer
Chapter 2.9, Problem 2.9ARQ
Explanation of Solution
Impact of free data storage and data communications on collaboration systems:
- In collaboration systems, there are many users and the each user produces some share of data and all this information should be stored in some storage area. Free data storage allows to store more information about the project.
- In collaboration systems it is not necessary that all the team members are present in person to work in a collaborative manner, so it is necessary to communicate the data like the edited files, task lists and other requirements of project to each and every member of the team.
Face to face communication:
- In face to face communication each and every team member needs to be present at the same place to conduct a meeting or any discussion, which is not convenient in many situations as it takes time and costs a lot.
- For example, if one of the team members is in Australia and has to attend a meeting in India then it will take time and even the flight and other travel expenses are to be considered.
- Similarly if people stuck in traffic, burning the fuel in the vehicle and many such things leads to loss of time and money which can be avoided by avoiding face to face meetings.
Today, where technologies like augmented reality and 3D applications are booming in the industrial market, it is predicted that there will be a time when workers will be using these technologies in each and aspect of their work. This will reduce the cost and time loss which was experienced in face to face meetings as augmented reality and 3D applications are going to make it possible for workers to holographically collaborate in face to face meetings from places around the world. In this technology, few objects are real and others are holograms, which will reduce the costs.
Holoportation:
From the time when Microsoft has demonstrated about holoportation to the people, they have been slowly understanding about the 3D holographic representations of other people.
- In this technology, a person can hear or see everything the other person does as if he/she is physically present in front of them.
- However, in reality the person may be present in some other place located in some other town or even in some other country, and is holoporting to work.
- By using this technology, virtual collaboration can be facilitated which will reduce the cost of travel and loss of time...
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Here is a clear background and explanation of the full method, including what each part is doing and why.
Background & Motivation
Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption.
Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled.
Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data.
What We Are Doing: Overview
We solve this using a Teacher–Student knowledge distillation framework:
We train a Teacher model on a clean and complete dataset where both inputs and labels are available.
We then use that Teacher to teach two separate Student models:
Student A learns from incomplete input (some sensor values missing).
Student B learns from incomplete labels (RUL labels missing…
here is a diagram code :
graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…
details explanation and background
We solve this using a Teacher–Student knowledge distillation framework:
We train a Teacher model on a clean and complete dataset where both inputs and labels are available.
We then use that Teacher to teach two separate Student models:
Student A learns from incomplete input (some sensor values missing).
Student B learns from incomplete labels (RUL labels missing for some samples).
We use knowledge distillation to guide both students, even when labels are missing.
Why We Use Two Students
Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher.
Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation).
Using two students allows each to specialize in…
Chapter 2 Solutions
Using MIS (9th Edition)
Ch. 2.6 - Prob. 1EGDQCh. 2.6 - Prob. 2EGDQCh. 2.6 - Prob. 3EGDQCh. 2.6 - Prob. 4EGDQCh. 2.6 - Prob. 5EGDQCh. 2.6 - Prob. 6EGDQCh. 2.6 - Prob. 7EGDQCh. 2.6 - Prob. 8EGDQCh. 2.9 - Prob. 1SGDQCh. 2.9 - Prob. 2SGDQ
Ch. 2.9 - Prob. 3SGDQCh. 2.9 - Prob. 4SGDQCh. 2.9 - Prob. 5SGDQCh. 2.9 - Prob. 1EVETCh. 2.9 - Prob. 2EVETCh. 2.9 - Prob. 3EVETCh. 2.9 - Prob. 4EVETCh. 2.9 - Prob. 5EVETCh. 2.9 - Prob. 6EVETCh. 2.9 - Prob. 2.1ARQCh. 2.9 - Prob. 2.3ARQCh. 2.9 - Prob. 2.4ARQCh. 2.9 - Prob. 2.5ARQCh. 2.9 - Prob. 2.6ARQCh. 2.9 - Prob. 2.7ARQCh. 2.9 - Prob. 2.8ARQCh. 2.9 - Prob. 2.9ARQCh. 2 - Prob. 2.1UYKCh. 2 - Prob. 2.2UYKCh. 2 - Prob. 2.3UYKCh. 2 - Prob. 2.5CE2Ch. 2 - Prob. 2.6CE2Ch. 2 - Prob. 2.7CE2Ch. 2 - Prob. 2.8CE2Ch. 2 - Prob. 2.9CS2Ch. 2 - Prob. 2.10CS2Ch. 2 - Prob. 2.11CS2Ch. 2 - Prob. 2.12CS2Ch. 2 - Prob. 2.13CS2Ch. 2 - Prob. 2.14CS2Ch. 2 - Prob. 2.15CS2
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