EBK USING MIS
10th Edition
ISBN: 8220103633642
Author: KROENKE
Publisher: YUZU
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Expert Solution & Answer
Chapter 3.8, Problem 1SGDQ
Explanation of Solution
Devices connected to the Internet in a home:
The number of devices connected to the internet may vary based on the individual’s needs and requirements. There are 5 devices connected to the internet in a home.
The devices connected to the Internet are listed below:
- Smart TV
- Light bulbs
- Refrigerators
- Air Conditioners
- Thermostat systems
Smart TV:
Smart Television is IoT enabled television sets, which are designed with internet features. This allows the user to access the internet applications, streaming providers and interactive services.
Light bulbs:
IoT enabled light bulbs makes use of a “bridge” which allows connecting the smart phone or tableting into the light bulbs. The light bulbs have been programmed based on the preference of lighting. Lights bulbs can be switched off by using remotely using the smart technologies.
Refrigerators:
Smart refrigerators generate the next iteration in food storage technology and improve the easiness of storing the food. The technology used in the refrigerators scan the barcode, intimates the expiration date, and sends the data to the phone.
Air conditioners:
IoT enabled air conditioners are connected to the smart phone which is used to tune temperature...
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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…
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Write two custom tags:
1) A single tag which prints a number (from 0-99) as words. Ex:
<abc:numAsWords val="32"/> --> produces: thirty-two 2) A paired tag which puts the body in a DIV with our team colors. Ex:
<abc:teamColors school="gophers" reverse="true"> <p>Big game today</p> <p>Bring your lucky hat</p> <-- these will be green text on blue background </abc:teamColors>
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Chapter 3 Solutions
EBK USING MIS
Ch. 3.3 - Prob. 1EGDQCh. 3.3 - Prob. 2EGDQCh. 3.3 - Prob. 3EGDQCh. 3.3 - Prob. 4EGDQCh. 3.7 - Prob. 1TARQCh. 3.7 - Prob. 2TARQCh. 3.7 - Prob. 3TARQCh. 3.7 - Prob. 4TARQCh. 3.8 - Prob. 1SGDQCh. 3.8 - Prob. 2SGDQ
Ch. 3.8 - Prob. 3SGDQCh. 3.8 - Prob. 4SGDQCh. 3.8 - Prob. 3.1ARQCh. 3.8 - Prob. 3.2ARQCh. 3.8 - Prob. 3.3ARQCh. 3.8 - Prob. 3.4ARQCh. 3.8 - Prob. 3.5ARQCh. 3.8 - Prob. 3.6ARQCh. 3.8 - Prob. 3.7ARQCh. 3.8 - Prob. 3.8ARQCh. 3 - Prob. 3.1UYKCh. 3 - Prob. 3.2UYKCh. 3 - Prob. 3.3UYKCh. 3 - Prob. 3.1CE3Ch. 3 - Prob. 3.4CS3Ch. 3 - Prob. 3.5CS3Ch. 3 - Prob. 3.6CS3Ch. 3 - Prob. 3.7CS3Ch. 3 - Prob. 3.8CS3Ch. 3 - Prob. 3.9CS3Ch. 3 - Prob. 3.10CS3Ch. 3 - Prob. 3.11CS3Ch. 3 - Prob. 3.12CS3Ch. 3 - Prob. 3.13MMLCh. 3 - Prob. 3.14MMLCh. 3 - Prob. 3.15MML
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