COVID-19 was announced as a global pandemic by the World Health Organization (WHO) in March 2020. With more than 31.3 million confirmed cases and over 965 thousand deaths recorded as of September 2020, it has inflicted catastrophic damage worldwide. The aim of this study is to develop an algorithm based on artificial intelligence (AI) and image processing techniques to identify COVID-19 patients with the aid of CT chest scan images. This study used a CT scan image dataset that is publically available for the researchers at Kaggle. We randomly extracted 27% of positive CT (pCT) images and 11% of negative CT (nCT) images from the original dataset. In the testing process, 120 of the test subjects in both nCT and pCT were used to validate the algorithm. Based on the experimental findings, the proposed COVID-19 detection algorithm shows promising results for the identification of COVID-19 patients with 90.83% accuracy at an average precision of 0.905.

Solar radiation or also referred to as solar power is the general expression for electromagnetic radiation emitted by the Sun. Direct solar radiation is an important factor in global solar radiation and is very influential in the efficiency evaluation of various applications for solar energy. For countries like Sri Lanka, installing a solar radiation instrument in rural areas is a challenge. Thus, both scientific and economically, measuring solar radiation without installing measuring instruments is an advantage. The aim of this study is to development of a mathematical model to predict solar radiation where solar radiation measurement instruments are not installed. The Artificial Neural Network (ANN) was used to verify the predictions of the mathematical model. Multiple Linear Regression (MLR) analysis was used for the development of a mathematical model to predict solar radiation. The model with the highest R2 value (0.5973) was chosen from the 127 equations as the best model that describes the solar radiation that reaches the surface of the earth. The dataset used for this study was meteorological data from the four month HI-SEAS weather station and are composed of ten attributes including date, time, radiation (H), temperature (Tair), pressure (P), humidity (φ), sunrise time, sunset time, wind direction (D), and speed (S). The angle of declination (δ) and sunshine hours (N) were calculated using the dataset. For the training of the neural network, 80 % of the data from the HI-SEAS dataset was used. The remaining data were used for testing both mathematical and ANN models. Results obtained from the multiple linear regression method and the ANN method was compared with the measured values. The experimental results suggested that the mathematical model was predicted the solar radiation with ±100 Wm-2 tolerance for both measured and ANN values.

MODBUS is a serial communication protocol use in industry, which developed by Modicon in 1979. The most common usage of the MODBUS communication protocol is the Programmable Logic Controllers (PLC). The aim of this study is to develop a system for acquiring and analyze measured values from the power measuring device and display them on C# developed GUI. The main objective is to develop an algorithm for MODBUS RTU communication protocol for data acquisition process. In this study, a single power measuring device was used with the aid of MODBUS RTU and RS-485 communication protocols, and data shown in the device was visualized on the C# developed GUI application and analyze them according to the user’s requirement. Data acquisitions from the holding registers were made by using the MODBUS Function Code three. The test results were observed and verified accuracy by performing several trials. Data was collected from the power measuring device which was attached to the industrial soft starter panel of a milling machine. Voltage, Current, Total Harmonic Distortion and Power Factor for each phase were retrieved to the C# devolved computer application and verify results with the real-time data of the power measuring device.

- Aluminum is the third most abundant element in the nature and extrusion is valuable due to commercial fenestration and manufacturing products. Aluminum profiles are causing severe damage during the fabrication and installation process. Sawing, drilling, and tapping are the most common mechanical damages cause in aluminum profiles when subjected to the organic coating. Aluminum is interacting with the Alkali Hydroxides (Ca(OH)2, NaOH, KOH, RbOH, and CsOH) during the cement-based installation process [1]. There are several companies that involving to the manufacturing of aluminum profiles in Sri Lanka. In Sri Lanka, the film applying process of aluminum profiles is carried out by human hand. Due to this problem, companies are looking for solutions to minimize time consumption and cost. The aim of this study is to find a possible solution and design a machine to protect the surface of the aluminum profiles. The objective is to minimize the time consuming and cost-effective method for the film application process. The system was tested with different profile shapes and results were analyzed with the existing manual method. We observed that the designed machine was 87.04% more efficient than the manual existing method in Sri Lanka.