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Geometry prediction of the antenna design using machine learning method
- 1. GEOMETRY PREDICTION OF THE ANTENNA DESIGN USING MACHINE LEARNING METHOD Agusriandi Eko Setijadi, S.T., M.T., Ph.D. Dr. Ir. Achmad Affandi, DEA agusriandi595@gmail.com INDONESIA, 11 December 2023
- 2. Digital, Global, Integrity Outline 01 Background 03 Methodology 04 Result and Discussion 02 Literature Review 05 Conclusion
- 3. Digital, Global, Integrity Problems of accuracy and effectiveness Design & Optimization issues Background
- 4. Digital, Global, Integrity Literature Review Principle of Predicting The principle of predicting is a method used in antenna design to predict the performance of an antenna system before it is built. This is done by using mathematical models and simulations to predict the antenna's radiation pattern, gain, and other important parameters. Applications in Antenna Design The principle of predicting is used extensively in antenna design for wireless communication systems. By using this method, engineers can design antennas that meet the specific requirements of a particular system, such as frequency range, bandwidth, and gain. This helps to ensure that the antenna system performs optimally and provides reliable communication.
- 5. Digital, Global, Integrity THERE IS A NEED FOR NEW METHODS THAT CAN PRODUCE PREDICTIONS FOR THE DESIGN AND OPTIMIZATION OF ANTENNA BY UTILIZING ML
- 6. Digital, Global, Integrity Methodology • Experiment • Dataset Preparation • Training Model • Testing and Validation • Result and Analysis • Conclusion
- 7. Digital, Global, Integrity Experiment • substrate = FR-4 • dielectric constant = 4.5 • thickness = 1.5 mm • Frequency = 13 GHz • Width = 6.253 mm • Length = 4.788 mm • Feed lines = 3.5 mm
- 8. Digital, Global, Integrity Dataset Preparation The success of a machine learning model in antenna design depends largely on the quality of the dataset used to train it. Here are some steps to prepare the dataset: • Collect data on antenna designs and their corresponding performance metrics, such as gain and radiation pattern. • Preprocess the data by normalizing or standardizing the input features and output metrics to ensure they are comparable and have similar ranges. • Split the dataset into training, validation, and test sets. The training set is used to train the model, the validation set is used to tune the hyperparameters and prevent overfitting, and the test set is used to evaluate the final performance of the model.
- 9. Digital, Global, Integrity Data Collection Collect data on existing antenna designs and their performance characteristics. Data Preprocessing Clean and format the collected data to ensure it is suitable for use in the machine learning model. Feature Selection Identify the most relevant features from the preprocessed data to use in the machine learning model. Training Model
- 10. Digital, Global, Integrity Testing and Validation Testing Testing involves measuring the performance of the designed antenna in a controlled environment. This can be done using specialized equipment such as anechoic chambers or network analyzers. The data collected from testing is used to validate the antenna design and make necessary adjustments. Validation Validation involves testing the designed antenna in real-world scenarios to ensure that it meets the required specifications and performs optimally. This can be done using simulation software or by deploying the antenna in a real-world environment. The data collected from validation is used to fine-tune the antenna design and ensure that it performs as expected.
- 11. Digital, Global, Integrity Results and Analysis Simulation Results Our simulations show that the antenna designs generated using machine learning algorithms outperform traditional designs by up to 20%. Optimization Results Our machine learning algorithm was able to optimize antenna designs for both size and performance, resulting in smaller and more efficient antennas. Performance Analysis Our analysis of antenna performance data shows that the machine learning- generated designs have a more stable and consistent performance across different operating conditions.
- 12. Digital, Global, Integrity Experiment and Simulation Result and Discussion
- 13. Digital, Global, Integrity s11 = s paramater sL = substrate length L = Length patch fw = feed width w = width patch The number of data rows collected 243 rows from several times the Sweep parameters Generate Dataset
- 14. Digital, Global, Integrity Training Model
- 15. Digital, Global, Integrity Comparison of results from experiments and simulations of ML
- 17. 5/12/2024 17 Digital, Global, Integrity Thank You
Editor's Notes
- Assalamu’alaikum wr.wb.. Good afternoon everyone Before we begin our presentation let me introduce myself.. My name is Agusriandi In this time, I’m going to share you about
- I will begin... Outline ... Fist of all
- Let’s move on to background.. Engineers use computer-aided design software and real-world testing to optimise the antenna design for the best performance. However, numerical methods provide effective results by using a combination of mathematical operations. This process has to be repeated every time a small change is made to the geometry. This is time consuming.. The other hands, The major problem of ML methods are accuracy and effecitivenss
- The ML methods and algorithms used to predict antenna geometry included linear regression, decision trees (random forests), polynomial regression, neural networks (deep learning) and support vector machines (SVMs).
- Therefore, THERE IS A NEED FOR NEW METHODS THAT CAN PRODUCE PREDICTIONS FOR THE DESIGN AND OPTIMIZATION OF ANTENNA BY UTILIZING ML in this study, the SVM was recommended due to its advantageous properties, which often converge to the global minimum rather than getting stuck in local minima when dealing with highly non-linear problems.
- The step of the methodology such as ecperiment, dataset
- in the experimental step, We set the properties of the design antenna as
- The next step... Dataset preparation included : collect data
- The training medel step, Here are some steps and others if possible
- The testing and validation step ... The Validation involves testing the designed antenna in real-world scenarios to ensure that it meets the required specifications and performs optimally. Testing Testing involves measuring the performance of the designed antenna in a controlled environment
- In this slide, I will show the results and discussion of this research, where The Top Figure is parameter values The bottom figure is result of simulation The right figure is S11 parameter
- The Left figure show dataset form parameter sweep in CST Suite Studio The right figure show set of the parameter sweep
- The Figure show the result of training model using SVM algorithm. The result prediction is fifteen point six eight
- The figure show comparison of result from expriments and simulation of ML. where red line is experiment and blue line is simulation ML
- I’d like to conclude my presentation.., this study has confirmed the effectiveness of Machine Learning (ML) in designing a simple microstrip patch antenna. Despite the small amount of data collected from the simulator. A correlation coefficient of 0.986 (zero point nine eight six) was obtained using the ML, namely rectangular patch antennas operating at a frequency of 13 GHz. The ML approach provides a superior support for the design of antennas with a higher degree of accuracy and efficiency compared to other methods