PowerMatlab via @vote
What kind of training program would you like us to offer in the near future?
anonymous poll
Comprehensive training program in power electrical engineering, covering basic to advanced levels β 66
πππππππ 37%
3. Combined training course on comprehensive power electrical engineering (beginner to advanced)and simulation of specialized pa β 62
πππππππ 35%
Training course on simulating a specialized paper β 51
πππππ 28%
π₯ 179 people voted so far.
anonymous poll
Comprehensive training program in power electrical engineering, covering basic to advanced levels β 66
πππππππ 37%
3. Combined training course on comprehensive power electrical engineering (beginner to advanced)and simulation of specialized pa β 62
πππππππ 35%
Training course on simulating a specialized paper β 51
πππππ 28%
π₯ 179 people voted so far.
π11β€2π―1
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How to Call Python from MATLAB
LinkedinPage: https://lnkd.in/eYTRE84s
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LinkedinPage: https://lnkd.in/eYTRE84s
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PowerMatlab via @vote
What level of education are you studying?
anonymous poll
3- Ph.D or higher β 231
πππππππ 60%
2- MSc β 121
ππππ 31%
1- Bsc β 34
π 9%
π₯ 386 people voted so far.
anonymous poll
3- Ph.D or higher β 231
πππππππ 60%
2- MSc β 121
ππππ 31%
1- Bsc β 34
π 9%
π₯ 386 people voted so far.
π9
PowerMatlab
https://www.linkedin.com/posts/mehdi-zareian-jahromi-ph-d-aaa7ba51_22-people-have-published-more-than-200-papers-activity-7208082629546385408-1RqM?utm_source=share&utm_medium=member_desktop
22 people have published more than 200 papers in 2024 (so far, we still have another six months to go)
We have been doing some research on another project and saw something that we found interesting. We'd be interested to know what you think.
In 2024, 22 authors (according to @Scopus
) have published over 200 papers. The table below shows the details (we decided not to show author names at this time).
The author who has published the most papers has authored 261 papers (as at 15 Jun 2024) meaning that he/she has published 1.56 papers every day this year (and counting).
If we exclude weekends (everybody has to rest, right?), then they have published 2.18 papers every day.
Serious question: Is it possible for anybody to contribute in a meaningful way (at least to warrant being at author) when they are publishing a paper every day?
Source: https://lnkd.in/d_czxF-T
We have been doing some research on another project and saw something that we found interesting. We'd be interested to know what you think.
In 2024, 22 authors (according to @Scopus
) have published over 200 papers. The table below shows the details (we decided not to show author names at this time).
The author who has published the most papers has authored 261 papers (as at 15 Jun 2024) meaning that he/she has published 1.56 papers every day this year (and counting).
If we exclude weekends (everybody has to rest, right?), then they have published 2.18 papers every day.
Serious question: Is it possible for anybody to contribute in a meaningful way (at least to warrant being at author) when they are publishing a paper every day?
Source: https://lnkd.in/d_czxF-T
lnkd.in
LinkedIn
This link will take you to a page thatβs not on LinkedIn
π14π6β€1
PowerMatlab
22 people have published more than 200 papers in 2024 (so far, we still have another six months to go) We have been doing some research on another project and saw something that we found interesting. We'd be interested to know what you think. In 2024, 22β¦
I believe none of these 22 individuals can simulate their own articles, nor can they create a basic program to even print their own names.βΊοΈπ
Sincerely π
Zareian Jahromi
Sincerely π
Zareian Jahromi
π20π5
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How to Seek Assistance for MATLAB Programming Using AI (ChatGPT)
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πΊJune 23rd is "International Women In Engineering Day"
πΉ A day to encourage young girls and women to enter the engineering profession.
πΉ It is also a day to recognize and appreciate all women engineers worldwide, and this year, one of the goals is "Investing in women's skills in engineering to accelerate progress."
Congratulations to all the esteemed women engineers in the PowerMatlab family.
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πΉ A day to encourage young girls and women to enter the engineering profession.
πΉ It is also a day to recognize and appreciate all women engineers worldwide, and this year, one of the goals is "Investing in women's skills in engineering to accelerate progress."
Congratulations to all the esteemed women engineers in the PowerMatlab family.
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Project Number (3110):
How to Utilize ChatGPT for Simulating Your Target Model
Example #1 : PSO Algorithm
Example #2 : FlyBack Converter
YouTube link : https://youtu.be/ebGHaakOupU
Instagram ID : https://www.instagram.com/power_matlab?r=nametag
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ππ
How to Utilize ChatGPT for Simulating Your Target Model
Example #1 : PSO Algorithm
Example #2 : FlyBack Converter
YouTube link : https://youtu.be/ebGHaakOupU
Instagram ID : https://www.instagram.com/power_matlab?r=nametag
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ππ
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Project Number (3111):
Free Download of MATLAB Simulation File for Noninvasive Online Condition Monitoring of Output Capacitorβs ESR and C for a Flyback Converter
YouTube link : https://youtu.be/9cTYRPTKcXw
Instagram ID : https://www.instagram.com/power_matlab?r=nametag
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ππ
Free Download of MATLAB Simulation File for Noninvasive Online Condition Monitoring of Output Capacitorβs ESR and C for a Flyback Converter
YouTube link : https://youtu.be/9cTYRPTKcXw
Instagram ID : https://www.instagram.com/power_matlab?r=nametag
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ππ
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Exploring the Diverse Types of Neural Networks in AI π€
In the rapidly evolving field of artificial intelligence, understanding the different types of neural networks is crucial. Each type offers unique capabilities and applications. Let's delve into the various kinds of neural networks:
Perceptron π§
The perceptron is the simplest form of a neural network, consisting of a single neuron. It is primarily used for binary classification tasks, distinguishing between two distinct classes.
Feed-Forward Networks (FFNs) β‘οΈ
Feed-forward networks are structured so that data flows in a single directionβfrom input to output. These networks are foundational in machine learning, useful for tasks such as prediction and classification.
Multi-Layer Perceptron (MLP) π
Multi-layer perceptrons extend the concept of the perceptron by incorporating one or more hidden layers between the input and output layers. This architecture allows MLPs to capture complex patterns and interactions within the data.
Radial Basis Function Networks (RBFNs) π―
RBF networks utilize radial basis functions as activation functions. They are particularly effective for classification and regression problems, leveraging the properties of these functions to model complex relationships.
Convolutional Neural Networks (CNNs) πΌ
Convolutional neural networks are designed for processing visual data. By applying convolutional layers, CNNs can automatically and adaptively learn spatial hierarchies of features from input images, making them indispensable for image and video recognition tasks.
Recurrent Neural Networks (RNNs) π
Recurrent neural networks are adept at handling sequential data due to their inherent structure, which allows them to maintain a 'memory' of previous inputs. This makes RNNs suitable for tasks such as time series forecasting, language modeling, and speech recognition.
Long Short-Term Memory Networks (LSTMs) β³
LSTMs are a specialized type of RNN designed to address the vanishing gradient problem. They excel at learning long-term dependencies, making them ideal for applications requiring the retention of information over extended periods, such as complex sequence prediction and natural language processing.
Power Electrical Developing Advanced Research (PEDAR) Group
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In the rapidly evolving field of artificial intelligence, understanding the different types of neural networks is crucial. Each type offers unique capabilities and applications. Let's delve into the various kinds of neural networks:
Perceptron π§
The perceptron is the simplest form of a neural network, consisting of a single neuron. It is primarily used for binary classification tasks, distinguishing between two distinct classes.
Feed-Forward Networks (FFNs) β‘οΈ
Feed-forward networks are structured so that data flows in a single directionβfrom input to output. These networks are foundational in machine learning, useful for tasks such as prediction and classification.
Multi-Layer Perceptron (MLP) π
Multi-layer perceptrons extend the concept of the perceptron by incorporating one or more hidden layers between the input and output layers. This architecture allows MLPs to capture complex patterns and interactions within the data.
Radial Basis Function Networks (RBFNs) π―
RBF networks utilize radial basis functions as activation functions. They are particularly effective for classification and regression problems, leveraging the properties of these functions to model complex relationships.
Convolutional Neural Networks (CNNs) πΌ
Convolutional neural networks are designed for processing visual data. By applying convolutional layers, CNNs can automatically and adaptively learn spatial hierarchies of features from input images, making them indispensable for image and video recognition tasks.
Recurrent Neural Networks (RNNs) π
Recurrent neural networks are adept at handling sequential data due to their inherent structure, which allows them to maintain a 'memory' of previous inputs. This makes RNNs suitable for tasks such as time series forecasting, language modeling, and speech recognition.
Long Short-Term Memory Networks (LSTMs) β³
LSTMs are a specialized type of RNN designed to address the vanishing gradient problem. They excel at learning long-term dependencies, making them ideal for applications requiring the retention of information over extended periods, such as complex sequence prediction and natural language processing.
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PowerMatlab
Dear friends
This channel shares all your needed matlab codes and simulation files in field of power electrical engineering. Everyone is welcome to download and use the codes and simulation files. Please let us know if you have any questions or comments.β¦
This channel shares all your needed matlab codes and simulation files in field of power electrical engineering. Everyone is welcome to download and use the codes and simulation files. Please let us know if you have any questions or comments.β¦
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Why is solar energy important?
π The Earth has infinite solar energy βοΈ!
In this fascinating video, Richard Kemp explains simply and clearly how solar panels convert sunlight into electricity.
Advantages of using solar panels include:
πReducing energy costs
πReducing air pollution
πProviding clean and unlimited energy
Watch the video to find out how solar energy can transform our future.
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π The Earth has infinite solar energy βοΈ!
In this fascinating video, Richard Kemp explains simply and clearly how solar panels convert sunlight into electricity.
Advantages of using solar panels include:
πReducing energy costs
πReducing air pollution
πProviding clean and unlimited energy
Watch the video to find out how solar energy can transform our future.
Power Electrical Developing Advanced Research (PEDAR) Group
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ACDCMicrogrids_3112.rar
7.5 MB
Project Number (3112): Free download of Matlab Simulation file for Hybrid AC/DC microgrid test system simulation: grid connected and Island Modes
Free training Video : ππ
YouTube link : https://youtu.be/XxCoB_Zi3ug
Instagram ID : https://www.instagram.com/power_matlab?r=nametag
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Free training Video : ππ
YouTube link : https://youtu.be/XxCoB_Zi3ug
Instagram ID : https://www.instagram.com/power_matlab?r=nametag
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Preventing step voltage in case of power cable breakage
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Dear Colleagues,
We are thrilled to announce that a Special Issue titled "AI-Based Modelling and Control of Power Systems" is now open for submissions.
This Special Issue is proudly supported by the Power Electrical Developing Advanced Research (PEDAR) Group and is hosted by the journal Processes (ISSN 2227-9717). This issue belongs to the "Energy Systems" section of the journal.
πMore details and submission entrance: https://www.mdpi.com/journal/processes/special_issues/N06O7BQX80
π Guest Editors:
Dr Mohammad Reza Maghami
Assoc. Prof. Javad Rahebi
Dr. Mehdi Zareian Jahromi
π Deadline for manuscript submissions: 03 March 2025.
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We are thrilled to announce that a Special Issue titled "AI-Based Modelling and Control of Power Systems" is now open for submissions.
This Special Issue is proudly supported by the Power Electrical Developing Advanced Research (PEDAR) Group and is hosted by the journal Processes (ISSN 2227-9717). This issue belongs to the "Energy Systems" section of the journal.
πMore details and submission entrance: https://www.mdpi.com/journal/processes/special_issues/N06O7BQX80
π Guest Editors:
Dr Mohammad Reza Maghami
Assoc. Prof. Javad Rahebi
Dr. Mehdi Zareian Jahromi
π Deadline for manuscript submissions: 03 March 2025.
Power Electrical Developing Advanced Research (PEDAR) Group
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