PROGRAMMABLE LOGIC CONTROLLERS (LOOSE PA
5th Edition
ISBN: 9781264206216
Author: Petruzella
Publisher: MCG
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Chapter 15.3, Problem 8RQ
Program Plan Intro
Preset:
- It is represented by PRE. It is the length of time that the timer should accumulate to achieve the preferred time delay.
- The value of PRE is stored as a binary number that is in “DINT” data type.
- The time base of PRE is always 1 msec.
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In the diagram, there is a green arrow pointing from Input C (complete data) to Transformer Encoder S_B, which I don’t understand. The teacher model is trained on full data, but S_B should instead receive missing data—this arrow should not point there. Please verify and recreate the diagram to fix this issue. Additionally, the newly created diagram should meet the same clarity standards as the second diagram (Proposed MSCATN). Finally provide the output image of the diagram in image format .
Please provide me with the output image of both of them . below are the diagrams code
make sure to update the code and mentionned clearly each section also the digram should be clearly describe like in the attached image. please do not provide the same answer like in other question . I repost this question because it does not satisfy the requirment I need in terms of clarifty the output of both code are not very well details
I have two diagram :
first diagram code
graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder…
Why I need ?
Chapter 15 Solutions
PROGRAMMABLE LOGIC CONTROLLERS (LOOSE PA
Ch. 15.1 - Prob. 1RQCh. 15.1 - Prob. 2RQCh. 15.1 - Prob. 3RQCh. 15.1 - Prob. 4RQCh. 15.1 - Prob. 5RQCh. 15.1 - Prob. 6RQCh. 15.1 - Prob. 7RQCh. 15.1 - Prob. 8RQCh. 15.1 - Prob. 9RQCh. 15.1 - Prob. 10RQ
Ch. 15.1 - Prob. 11RQCh. 15.1 - Compare the accessibility of program scope and...Ch. 15.1 - Prob. 13RQCh. 15.1 - What is the difference between a produced tag and...Ch. 15.1 - Prob. 15RQCh. 15.1 - State the data type used for each of the...Ch. 15.1 - Describe the make-up of a predefined structure.Ch. 15.1 - Describe the make-up of a module-defined...Ch. 15.1 - Describe the make-up of a user-defined structure.Ch. 15.1 - Prob. 20RQCh. 15.1 - Prob. 21RQCh. 15.1 - Prob. 22RQCh. 15.1 - Prob. 23RQCh. 15.2 - Prob. 1RQCh. 15.2 - Prob. 2RQCh. 15.2 - Prob. 3RQCh. 15.2 - Prob. 4RQCh. 15.2 - Prob. 5RQCh. 15.2 - Prob. 6RQCh. 15.2 - Prob. 7RQCh. 15.2 - Prob. 8RQCh. 15.2 - Prob. 9RQCh. 15.2 - Prob. 10RQCh. 15.2 - Prob. 11RQCh. 15.2 - Extend control of the original ControlLogix...Ch. 15.2 - Prob. 3PCh. 15.3 - Prob. 1RQCh. 15.3 - Prob. 2RQCh. 15.3 - Prob. 3RQCh. 15.3 - Prob. 4RQCh. 15.3 - Prob. 5RQCh. 15.3 - Prob. 6RQCh. 15.3 - Prob. 7RQCh. 15.3 - Prob. 8RQCh. 15.3 - Prob. 9RQCh. 15.3 - Prob. 10RQCh. 15.3 - Prob. 11RQCh. 15.3 - Prob. 12RQCh. 15.3 - Modify the original CLX ten-second TON timer...Ch. 15.3 - Prob. 2PCh. 15.3 - Prob. 3PCh. 15.3 - Prob. 4PCh. 15.3 - Prob. 5PCh. 15.3 - Prob. 6PCh. 15.4 - Prob. 1RQCh. 15.4 - Prob. 2RQCh. 15.4 - Prob. 3RQCh. 15.4 - Prob. 4RQCh. 15.4 - Prob. 5RQCh. 15.4 - Prob. 6RQCh. 15.4 - Prob. 7RQCh. 15.4 - Prob. 1PCh. 15.4 - Prob. 2PCh. 15.5 - Prob. 1RQCh. 15.5 - Prob. 2RQCh. 15.5 - Prob. 3RQCh. 15.5 - Prob. 4RQCh. 15.5 - Prob. 5RQCh. 15.5 - Construct a ControlLogix ladder rung with compare...Ch. 15.5 - Prob. 2PCh. 15.5 - A single pole switch is used in place of the two...Ch. 15.6 - Prob. 1RQCh. 15.6 - Name the four basic elements of an FBD.Ch. 15.6 - Prob. 3RQCh. 15.6 - Prob. 4RQCh. 15.6 - Prob. 5RQCh. 15.6 - Prob. 6RQCh. 15.6 - Prob. 7RQCh. 15.6 - Prob. 8RQCh. 15.6 - Prob. 9RQCh. 15.6 - Prob. 10RQCh. 15.6 - Prob. 11RQCh. 15.6 - How is a function block feedback loop created?Ch. 15.6 - Prob. 13RQCh. 15.6 - Prob. 14RQCh. 15.6 - Prob. 1PCh. 15.6 - Prob. 2PCh. 15.6 - Prob. 3PCh. 15.6 - Prob. 4PCh. 15.6 - Prob. 5P
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- Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…arrow_forwardPlease provide me with the output image of both of them . below are the diagrams code I have two diagram : first diagram code graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…arrow_forwardI'm reposting my question again please make sure to avoid any copy paste from the previous answer because those answer did not satisfy or responded to the need that's why I'm asking again The knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges…arrow_forward
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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…arrow_forwarddetails 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…arrow_forwardWe are doing a custom JSTL custom tag to make display page to access a tag handler. 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> Details: The attribute for numAsWords will be just val, from 0 to 99 - spelling, etc... isn't important here. Print "twenty-six" or "Twenty six" ... . Attributes for teamColors are: school, a "required" string, and reversed, a non-required boolean. - pick any four schools. I picked gophers, cyclones, hawkeyes and cornhuskers - each school has two colors. Pick whatever seems best. For oine I picked "cyclones" and red text on a gold body - if…arrow_forward
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