Microsoft Visual C#: An Introduction to Object-Oriented Programming, Loose-leaf Version
Microsoft Visual C#: An Introduction to Object-Oriented Programming, Loose-leaf Version
7th Edition
ISBN: 9781337685771
Author: Joyce Farrell
Publisher: Cengage Learning
Question
Book Icon
Chapter 6, Problem 1E
Program Plan Intro

Program Plan

1. Below mentioned variables are used

  • intArrayinteger array that holds 10 integers as array elements.
  • inputinput value for array element read from the console.
  • position − position of array element given as input from the user to search for the element.
  • Pos −position value converted to int type.

Program description:

This program is for creating a console application that creates an array of ten elements and these elements are given as input through the console. It offers four options to the user that is to view the array elements in order and to view by the position of the element or to view in the reverse order. The fourth option is to quit the application.

Blurred answer
Students have asked these similar questions
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…
Knowledge Booster
Background pattern image
Similar questions
SEE MORE QUESTIONS
Recommended textbooks for you
Text book image
Microsoft Visual C#
Computer Science
ISBN:9781337102100
Author:Joyce, Farrell.
Publisher:Cengage Learning,
Text book image
EBK JAVA PROGRAMMING
Computer Science
ISBN:9781337671385
Author:FARRELL
Publisher:CENGAGE LEARNING - CONSIGNMENT
Text book image
Programming with Microsoft Visual Basic 2017
Computer Science
ISBN:9781337102124
Author:Diane Zak
Publisher:Cengage Learning
Text book image
Programming Logic & Design Comprehensive
Computer Science
ISBN:9781337669405
Author:FARRELL
Publisher:Cengage
Text book image
C++ Programming: From Problem Analysis to Program...
Computer Science
ISBN:9781337102087
Author:D. S. Malik
Publisher:Cengage Learning
Text book image
C++ for Engineers and Scientists
Computer Science
ISBN:9781133187844
Author:Bronson, Gary J.
Publisher:Course Technology Ptr