https://orcid.org/0000-0003-1742-5526Tuesday, 27 April 2021
Sunday, 25 April 2021
How to Compute Switching Losses in Power System Thermal Network Inverter...
Modern Power Converters Modeling Techniques _ Using Matlab Simulink
Modeling Techniques Description
You can run this simulation using one of the following modeling techniques:
1. Switching Devices: Converters are modeled using standard SPS power switches and diodes controlled by firing pulses which are produced by the PWM generators.
2. Switching Function: Converters are modeled using a switching-function model controlled by firing pulses which are produced by the PWM generators.
3. Switching Function (PWM averaging): Converters are modeled using a switching-function model controlled by averaging the firing pulses produced by the PWM generators over a specified period.
4. Reference-Voltage (Uref or D-Controlled): Converters are modeled using a switching-function model directly controlled by the reference voltage (Uref) or the duty-cycle (D). PWM generators are not required.
Technique 1 is the most accurate modeling technique, while technique 4 yields to the fastest simulation. Techniques 2 and 3 are well-suited for real-time simulation.
Simulation
During the simulation, the DC variable load will vary from 125 kW to 350 kW at 5 Hz. At 0.5 s, the DC motor speed setpoint is changed from 1200 to 800 RPM. At 0.6 s, the STATCOM reference (Qref) is changed from -1 Mvar to +1.5 Mvar.
Run the simulation and observe the resulting signals on the various scopes. Select a different modeling technique and rerun the simulation, comparing results with previous runs.
Click Here to download the Simulink mOdel: https://drive.google.com/file/d/1MoJl...
Temperature Control in a Heat Exchanger Using Matlab Simulink
Modeling & Analysis of Vehicle HVAC System using MATLAB Simulink
Thursday, 22 April 2021
Wednesday, 21 April 2021
Design & Analysis of Small Signal Bipolar Transistor Model _Common-Emitt...
Design & Analysis of Noninverting Amplifier using Op-Amp_ MATLAB Simulink
Design & Analysis of Inverting Amplifier using Op-Amp_ MATLAB Simulink ...
Design & Analysis of Full-Wave Bridge Rectifier_Using MATLAB Simulink
This example shows an ideal AC transformer plus full-wave bridge rectifier. It converts 120 volts AC to 12 volts DC. The transformer has a turns ratio of 14, stepping the supply down to 8.6 volts rms, i.e. 8.6*sqrt(2) = 12 volts pk-pk. The full-wave bridge rectifier plus capacitor combination then converts this to DC. The resistor represents a typical load.
The model can be used to size the capacitor required for a specified load. For a given size of capacitor, as the load resistance is increased, the ripple in the DC voltage increases. The model can also be used to drive an application circuit in order to assess the effect of the ripple.
Design & Analysis of Finite-Gain Op-Amp_Matlab Based Approach
Dining Philosophers Problem_Matlab Based Approach
Design & Analysis of Differentiator using OP Amp_Matlab Simulink
Design & Analysis of Band-Limited Op-Amp_Using Matlab Simulink
Monday, 19 April 2021
Design & Simulation of Oxygen Concentrator device coupled to a lung model.
This example models an oxygen concentrator device coupled to a lung model. One of the two sieves filters out nitrogen from the air to produce concentrated oxygen in the product tank. The two sieves switches periodically so that while one sieve is filtering, the other can purge the adsorbed nitrogen. When the lung model inhales, some of the oxygen-rich gas from the product tank is mixed into the inspiratory flow.
This model shows one use case of modifying the default properties in the Moist Air Properties (MA). The default "dry air" has been replaced with oxygen and the default "trace gas" has been replaced with nitrogen. This way, the Controlled Trace Gas Source (MA) block can be used to remove "trace gas" (i.e., nitrogen) from the flow through the sieve.
The lungs are represented by a Translational Mechanical Converter (MA), which converts pressure into translational motion. By setting the Interface cross-sectional area to unity, displacement in the mechanical translational network becomes a proxy for volume changes, force becomes a proxy for pressure, spring constant becomes a proxy for respiratory elastance, and damping coefficient becomes a proxy for respiratory resistance.
The device has two modes of operation: continuous or pulsating. The simulation starts in continuous mode, which delivers constant oxygen-rich flow to the lung model. At t = 70 s, the simulation switches to pulsating mode, which synchronizes oxygen delivery with inspiration. State Flow™ is used to estimate the breaths per minute and to control the conserving valve in the device.
Speed Cruise Control System Using Simulink® and Stateflow®
Airport Conveyer Belt Control System
Design & Simulation of Fuel Tank Filling Station Using MATLAB Simulink
Description
This example shows a hybrid system with both continuous time and discrete event sections. The discrete event part models tanks, represented by entities, which are being queued and need to be filled up. Each tank has a "Capacity" attribute. The continuous time part models the process of filling up a tank, modeled by an Integrator. When a tank is filled to capacity, this event can be detected by a Hit Crossing block, which will generate a message corresponding to this event. The generated message will trigger the server to release the tank.
Structure of the Model
The model includes the following components:
Tank Generator: Generates tanks periodically with each tank having an arbitrarily assigned Capacity attribute.
Waiting Queue: Queues tanks waiting to be filled
Fill This Tank: Serves tanks and calls into the Simulink Function
startFillingto pass the tank's capacity attribute to the time-based section of the model.
Tank Filling: Models the process of filling each tank up to capacity
Sensor: Detects when the amount filled in the tank has reached capacity and when this happens, sends a message to the discrete-event section of the model. Sensor serves as a bridge between the time-based section and even-based section.
Processor: Receives message generated from the Sensor and decides which tank to be released from the Server. It then calls the Simulink Function named
releaseto generate a release message for a specific tank.
Selection Gate: Receives a release message, and in response, opens the gate to let the specific tank through.
Configure Demo: Sets the number of gas pumps in the gas station and turns on/off of the animation. To show the animation, please use a gas pump number between 1 and 20.
Domain Crossings Between Time Domain and Event Domain
SimEvents automatically handles any exchange of data across the time and event domains by automatically inserting gateways where needed. These positions are annotated in the model using E. In this model, a gateway has been inserted at the input port of the Entity Queue block that is connected to the Hit Crossing block since it receives a message from the time domain section of the model.
Results
The Scope block labeled "Fill Process" and "Trucks leaving after fill" shows the results of the simulation.
If Show Animation check box is selected in Configure Demo, an animation window appears for visualizing the demo. A screenshot of the animation with four gas pumps is shown below:
Design & Simulation of a Medical Device _Hematology Diagnostic Instrumen...
· This example shows how to conduct automated tests to model a medical device that analyzes biology samples. A medical device contains:· Samples to be analyzed · Reagent bottles
Design & Simulation of Medical Ventilator with Lung Model_ Using MATLAB ...
This example models a positive-pressure medical ventilator system. A preset flow rate is supplied to the patient. The lungs are modeled with the Translational Mechanical Converter (MA), which converts moist air pressure into translational motion. By setting the Interface cross-sectional area to unity, displacement in the mechanical translational network becomes a proxy for volume, force becomes a proxy for pressure, spring constant becomes a proxy for respiratory elastance, and damping coefficient becomes a proxy for respiratory resistance.
The exchange of oxygen and carbon dioxide in the moist air mixture is not currently modeled.Sunday, 4 April 2021
Cisco Packet Tracer Software based Videos
1)
Design and
implementation of Wired and Wireless Networking Protocols Using Cisco Packet
Tracer
https://youtu.be/RTEposDYIbghttps://youtu.be/RTEposDYIbg
2)
Implementation of
Networking Protocols Using Cisco Packet Tracer
3)
Simulation of Smart
Home Network using IoT devices. Simulation of Smart Home Network using IoT
devices.
4)
AI enabled IoT based
Smart Home and Smart Health care monitoring system Using Cisco Packet Tracer
5)
MCU & SBC
programming for smart IoT devices_ Using Cisco Packet Tracer
Filter Design & Analysis using Matlab
1)
Matlab Interactive
Tool for Filter Design and Analysis (filterDesigner)
2)
Filter Design and
Analysis Using Matlab_Part 2
3)
Design &
Implementation of Programmable FIR Filter with HDL Code Generation Project _
Logic Analyzer
4)
Design of Area
Efficient IIR Polyphase filter | Using Matlab DSP System Toolbox
Modeling & Analysis of Artificial Neural Network & Fuzzy Logic Systems
Using Matlab
1)
Introduction to
Neural Networks | Matlab Based Approach
2)
Artificial Neural
Network Tools Using Matlab_nftool
3)
NARX Neural Network
Time Series Prediction and modeling Using Matlab
4)
NARX _Prediction of
Pollution Load for Next 6 Months
5)
Shallow Neural
Network Time Series prediction and modeling using Matlab
6)
Solving a Shallow
Neural Network Curve Fitting Problem using Matlab
7)
Pattern Recognition
and classification tool for Artificial Neural Network Using Matlab
8)
Solve a Pattern
Recognition Problem with a two layer feed forward network using Matlab https://youtu.be/85T5aUoQu_s
9)
Solve a Clustering
Problem with a Self-Organizing Map (SOM) Networks
10)
Solving a
Clustering problem with Self Organizing Map (SOM) Neural Network using Matlab https://youtu.be/oBvSNQi_P-0
11) Design and Simulate Fuzzy Logic Systems Using
Matlab
Matlab for Machine Learning & Deep Learning
1)
Introduction to
Machine Learning | Deep Learning Applications using Matlab
2)
Introduction to
Machine Learning | With Matlab Distribution Fitter App
3)
Introduction to
Machine Learning | A Matlab based Approach_Regression Learner App https://youtu.be/ACus8yfmBFY
4)
Introduction to
Machine Learning | A Matlab Based Approach_Classification Learner App https://youtu.be/gTTo8RuPEMg
5)
Deep Learning for
Image Processing | Matlab Based Approach
6)
Deep Learning for
Natural Image Processing | Matlab based approach
7)
Deep Learning based
Vehicle Detection_Using Gaussian Mixture Models
8)
Deep learning based
implementation of Automated Text Detection Algorithm | Matlab Based Approach https://youtu.be/IkChOPUCPRE
9)
Transfer Learning
Using AlexNet https://youtu.be/viPfeQKG2fc
10) Deep
Learning Based Image Classification | Using AlexNet Convolutional Neural
Network https://youtu.be/hsZLHcrwI7w
11) Fit a
Distribution Using the Distribution Fitter App | Matlab Based Approach
12) Train
Regression Trees Using Regression Learner | Matlab app
13) Train
Decision Trees Using Classification Learner app | Matlab based approach|