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 6, Problem 1IQ
Explanation of Solution
Answer to interviewer question:
The job seeker is well known about substation equipment. The substation equipment is used in the fields of electrical generation, transmission, and distribution system. This equipment has assembled with electrical components like transformers, auxiliaries, switchgear, etc. This equipment is used many things they are:
- Protecting the system of transmission
- Controlling the exchange of energy
- Ensuring the steady state and transient stability.
SAP for utilities:
“Yes”, the job seeker was worked with SAP for utilities. SAP stands for Systems, Applications and Products in data processing. The SAP utilities is used to manage and bill residential, commercial, and prospective customers. The job seeker also managed the data which is fixed for long period of time in SAP.
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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 6 Solutions
Essentials of MIS (13th Edition)
Ch. 6.3 - Identify the problem in this case study. To what...Ch. 6.3 - Prob. 2CQ1Ch. 6.3 - Prob. 4CQ1Ch. 6.5 - Prob. 1CQ2Ch. 6.5 - What people, organization, and technology issues...Ch. 6.5 - Prob. 3CQ2Ch. 6.5 - Prob. 4CQ2Ch. 6 - Prob. 1IQCh. 6 - Prob. 2IQCh. 6 - Prob. 3IQ
Ch. 6 - Prob. 4IQCh. 6 - Prob. 1RQCh. 6 - Prob. 2RQCh. 6 - Prob. 3RQCh. 6 - Prob. 4RQCh. 6 - Prob. 5DQCh. 6 - Prob. 6DQCh. 6 - Prob. 7DQCh. 6 - Prob. 8HMPCh. 6 - Prob. 9HMPCh. 6 - Prob. 11HMPCh. 6 - Prob. 13CSQCh. 6 - Prob. 14CSQCh. 6 - Prob. 15CSQCh. 6 - Prob. 16CSQCh. 6 - Prob. 17MLMCh. 6 - Prob. 18MLM
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