Essentials of MIS (13th Edition)
13th Edition
ISBN: 9780134802756
Author: Kenneth C. Laudon, Jane Laudon
Publisher: PEARSON
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Expert Solution & Answer
Chapter 2.2, Problem 3CQ1
Explanation of Solution
System illustrated in the given case study:
The system which is discussed in the given case study is “NFL’s Player Tracking System”.
- This system is developed by the Zebra Technologies, to collect detailed data on a player, team performance, sales, and fan behavior to help out in making decisions which will be in needed in every aspect of the business in NFL (National Football League).
Source of data:
NFL’s Player Tracking System gathers data from the players using nickel-sized RFID (Radio Frequency Identification) tags placed beneath the shoulder pads. This tag will track every move the player makes and transmit data to the 20 data receivers placed in the lower and upper levels of the stadium.
Impacts from the data gathered:
NFL’s Player Tracking System records speed, direction and location of the player on the field, it also collects how far they ran, jogs, spring or walked on the field...
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Note : please avoid using AI answer the question by carefully reading it and provide a clear and concise solutionHere 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…
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…
Chapter 2 Solutions
Essentials of MIS (13th Edition)
Ch. 2.2 - What Kinds of systems are illustrated in this case...Ch. 2.2 - Prob. 2CQ1Ch. 2.2 - Prob. 3CQ1Ch. 2.2 - Prob. 4CQ1Ch. 2.3 - Prob. 1CQ2Ch. 2.3 - Prob. 2CQ2Ch. 2.3 - Prob. 3CQ2Ch. 2 - Prob. 1IQCh. 2 - Prob. 2IQCh. 2 - Prob. 3IQ
Ch. 2 - Prob. 4IQCh. 2 - Prob. 5IQCh. 2 - Prob. 6IQCh. 2 - Prob. 1RQCh. 2 - Prob. 2RQCh. 2 - Prob. 3RQCh. 2 - Prob. 4RQCh. 2 - Prob. 5DQCh. 2 - Prob. 6DQCh. 2 - Prob. 7DQCh. 2 - Prob. 8HMPCh. 2 - Prob. 9HMPCh. 2 - Prob. 12CTPCh. 2 - Prob. 13CSQCh. 2 - Prob. 14CSQCh. 2 - Prob. 15CSQCh. 2 - Prob. 16MLMCh. 2 - Prob. 17MLM
Knowledge Booster
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