Investigation on air + CO2 gas mixtures under lightning impulse

This project aims to investigate the breakdown level of gas mixtures (air + CO2) under lightning impulse. Specifically, this project also reveals the breakdown characteristics of air, carbon dioxide (CO2) and the gas mixture (air + CO2) with the purpose of making a comparison. The gas mixture has a ratio of 70% of CO2 and 30% of air. These gases were tested inside the pressure vessel and subjected to 1 bar (abs) pressure. Standard (1.2/50 μs) lightning impulse was applied alongside with three electrode arrangements. The gap distance between electrodes is also varied ranging from 0.5 cm to 2 cm. It was found that highest U50 was recorded at the furthest gap length and when the insulating medium used was CO2. The rising trend of U50 is more obvious at sphere gaps as it provides more uniform fields. This U50 was calculated by using up and down method. Moreover, the highest maximum electric field (Emax) was found at non-uniform field gap namely rod-sphere gaps whereas the values were obtained with the help of FEMM simulation software. The Emax of rod-sphere when the air is used as an insulating medium has revealed that it is directly proportional to the gap length in which probably due to the self-restoration of air at the non-uniform field is less compared to other gas hence causing the dropoff insulation strength. As the gap distance between electrodes is small, the highest field utilization factor was recorded which is 0.93 (5 cm sphere), 0.86 (2 cm sphere) and 0.11 (rod-sphere) as a result of the field dispersed uniformly at short gaps. It was also noticed that the Emax is inversely proportional to the field utilization factor (η) whereas the risen of η has a huge impact to the value of Emax. However, this project is restricted to normal room temperature (26° Celsius) and no humidity factor is considered

Deep Learning Based for Cryptocurrency Assistive System

Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognized as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency investments, many Alternative Coins (Altcoins) resurfaced to mimic the fiat currency. There are several methods to forecast cryptocurrency prices that have been widely used in forecasting fiat and stock prices. Artificial Intelligence (AI) ,Machine Learning(ML) and Deep Learning(DL) provide a different perspective on how investors can estimate crypto price trend and movement. In this paper, as cryptocurrency price is time-dependent, Recurrent Neural Network (RNN) is presented due to RNN’s nature, which is well suited for Time Series Analysis (TSA). The topology of the proposed RNN model consists of three stages which are model groundwork, model development, and testing and optimization. The RNN architecture is extended to three different models specifically Long Short-Term Memory (LSTM), Gat-ed Recurrent Unit (GRU), and Bi-Directional Long Short-Term Memory (LSTM). There are a few hyperparameters that affect the accuracy of the deep learning model in predicting cryptocurrency prices. Hyperparameter tuning set the basis for optimizing the model to improve the accuracy of cryptocurrency prediction. Next, the models were tested with data from different coins listed in the cryptocurrency market. Then, the model was experiment-ed with different input features to figure out how accurate and robust these models in predicting the cryptocurrency price. GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Executional Time, scoring 2.2201, 0.8076, and 200s using the intraday trading strategy as input features

Deep Learning Based for Cryptocurrency Assistive System

Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognized as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency investments, many Alternative Coins (Altcoins) resurfaced to mimic the fiat currency. There are several methods to forecast cryptocurrency prices that have been widely used in forecasting fiat and stock prices. Artificial Intelligence (AI) ,Machine Learning(ML) and Deep Learning(DL) provide a different perspective on how investors can estimate crypto price trend and movement. In this paper, as cryptocurrency price is time-dependent, Recurrent Neural Network (RNN) is presented due to RNN’s nature, which is well suited for Time Series Analysis (TSA). The topology of the proposed RNN model consists of three stages which are model groundwork, model development, and testing and optimization. The RNN architecture is extended to three different models specifically Long Short-Term Memory (LSTM), Gat-ed Recurrent Unit (GRU), and Bi-Directional Long Short-Term Memory (LSTM). There are a few hyperparameters that affect the accuracy of the deep learning model in predicting cryptocurrency prices. Hyperparameter tuning set the basis for optimizing the model to improve the accuracy of cryptocurrency prediction. Next, the models were tested with data from different coins listed in the cryptocurrency market. Then, the model was experiment-ed with different input features to figure out how accurate and robust these models in predicting the cryptocurrency price. GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Executional Time, scoring 2.2201, 0.8076, and 200s using the intraday trading strategy as input features

Proceedings of International Technical Postgraduate Conference 2022

This conference proceedings contains articles on the various research ideas of the academic & research communities presented at the International Technical Postgraduate Conference 2022 (TECH POST 2022) that was held at Universiti Malaya, Kuala Lumpur, Malaysia on 24-25 September 2022. TECH POST 2022 was organized by the Faculty of Engineering, Universiti Malaya. The theme of the conference is “Embracing Innovative Engineering Technologies Towards a Sustainable Future”.  TECH POST 2022 conference is intended to foster the dissemination of state-of-the-art research from five main disciplines of Engineering: Electrical Engineering, Biomedical Engineering, Civil Engineering, Mechanical Engineering, and Chemical Engineering. The objectives of TECH POST 2022 are to bring together innovative researchers from all engineering disciplines to a common forum, promote R&D activities in Engineering, and promote the dissemination of scientific knowledge and research know-how between researchers, engineers, and students. Conference Title: International Technical Postgraduate Conference 2022Conference Acronym: TECH POST 2022Conference Date: 24-25 September 2022Conference Location: Faculty of Engineering, Universiti Malaya, Kuala Lumpur Malaysia (Hybrid Mode)Conference Organizers: Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia