Browsing by Author "Perera, J"

Now showing 1 - 7 of 7

Open Access
Effects of Manufactured Sand on the Properties of Normal and High Strength Concrete
(SLIIT, 2022-02-11) Perera, J; Premadasa, R
Manufactured Sand (MS) has been introduced as a very effective fine aggregate and is being widely used in various construction activities. Large amounts of Manufactured Sand Fines (MSF) that are less than 75 µm in particle size, are produced during the production process. Costs are incurred in separating these fines from the crushed stone and are then dumped in landfills, thus causing serious environmental issues. Studies on MSF are not well established and a handful has been done on High Strength Concrete (HSC). The key objectives of this study were to study and compare the effects that MSF have on the properties of Normal Strength Concrete (NSC) and HSC and to propose effective fines percentages that could be incorporated in them. Tests were carried out by partial replacement of MS with fines in proportions of 10%, 15% and 20% for C30 and C60 concrete and were compared with the control mixes that contained 3.36% MSF. It was identified that a 15% replacement of MSF produced effective results with the highest compressive, splitting tensile and flexural strength results and minimum water absorption in both NSC and HSC. At 15% fines content, a strength of 35.3 MPa and 63.3 MPa was achieved by the C30 and C60 concretes respectively. However, the increment of fines decreased the workability significantly. The microstructure analysis proved the densification of the microstructure at 15% MSF content. The cost analysis showed that the availability of high fines content can deduct the cost of NSC by 1.8% and HSC by 1.6%. The 10% - 15% range was identified as the most effective fines content range that can be incorporated in NSC and HSC. Results of this study can contribute to develop concrete with better performance while addressing several environmental and cost issues related to the concrete industry.
Embargo
FarmCare: Location-based Profitable Crop Recommendation System with Disease Identification
(IEEE, 2022-12-09) Weerasooriya, W.M.M.S; Wanigaratne, A.D; De Silva, H.G.O; Hansaka, S.A.H; Perera, J; Rukgahakotuwa, L
Sri Lanka is an agricultural country since ancient times. Today’s agriculture field is in a dangerous situation because farmers are losing their yield. There are many factors to consider when planting crops like rainfall, temperature, soil conditions, future prices, diseases, etc. We decided to help them through the android application we are making. Here we identified four main problems. First, it was wrong crop cultivation. This is the main reason crops and cultivation are destroyed. To give a solution to that problem, we suggest the five most suitable crops to cultivate according to their location. The second problem is a lack of knowledge about future market prices. As a solution to that problem, we predict prices for each cop for the next 12 months. Another problem is an inability to sell their product at a reasonable price. Here, we directly connect buyers and sellers by removing intermediaries. The last problem is the difficulty to identify diseases affected by crops. Using our mobile app farmers can identify which disease affected their crops by uploading an image to the app. To give solutions to the above-mentioned problems Machine Learning algorithms are used like Random Forest, k-means clustering, and Convolution Neural Network algorithms.
Open Access
A Graph Pointer Network-Based Multi-Objective Deep Reinforcement Learning Algorithm for Solving the Traveling Salesman Problem
(MDPI, 2023-01-13) Perera, J; Liu, S.H; Mernik, M; Črepinšek, M; Ravber, M
Traveling Salesman Problems (TSPs) have been a long-lasting interesting challenge to researchers in different areas. The difficulty of such problems scales up further when multiple objectives are considered concurrently. Plenty of work in evolutionary algorithms has been introduced to solve multi-objective TSPs with promising results, and the work in deep learning and reinforcement learning has been surging. This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for multi-objective TSPs. The MODGRL improves an earlier multi-objective deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. Such improvements allow MODGRL to be trained on a small-scale TSP, but can find optimal solutions for large scale TSPs. NSGA-II, MOEA/D and SPEA2 are selected to compare with MODGRL and DRL-MOA. Hypervolume, spread and coverage over Pareto front (CPF) quality indicators were selected to assess the algorithms’ performance. In terms of the hypervolume indicator that represents the convergence and diversity of Pareto-frontiers, MODGRL outperformed all the competitors on the three well-known benchmark problems. Such findings proved that MODGRL, with the improved graph pointer network, indeed performed better, measured by the hypervolume indicator, than DRL-MOA and the three other evolutionary algorithms. MODGRL and DRL-MOA were comparable in the leading group, measured by the spread indicator. Although MODGRL performed better than DRL-MOA, both of them were just average regarding the evenness and diversity measured by the CPF indicator. Such findings remind that different performance indicators measure Pareto-frontiers from different perspectives. Choosing a well-accepted and suitable performance indicator to one’s experimental design is very critical, and may affect the conclusions. Three evolutionary algorithms were also experimented on with extra iterations, to validate whether extra iterations affected the performance. The results show that NSGA-II and SPEA2 were greatly improved measured by the Spread and CPF indicators. Such findings raise fairness concerns on algorithm comparisons using different fixed stopping criteria for different algorithms, which appeared in the DRL-MOA work and many others. Through these lessons, we concluded that MODGRL indeed performed better than DRL-MOA in terms of hypervolumne, and we also urge researchers on fair experimental designs and comparisons, in order to derive scientifically sound conclusions.
Embargo
A gyroscopic data based pedometer algorithm with adaptive orientation
(IEEE, 2018-06-12) de Silva, R; Perera, J; Abeysingha, C. P; Abhayasinghe, N
Orientation of an Inertial Measurement Unit (IMU) relative to earth is a critical factor to the step detection in gyroscopic data based pedometer algorithm. The orientation of the IMU will be often subjected to change while using the small scale electronic pedometers. Existing fixed axis gyroscopic data based pedometer algorithm may not be suitable to implement in the modern small scale embedded pedometer applications. In this paper we have developed an advanced version of the gyroscopic data based pedometer algorithm which can dynamically adjust for the changing orientation of the IMU. Step detection component of the proposed algorithm is based on the gyro readings and the orientation detection is based on accelerometer readings. The algorithm employs the gravity vector and linear acceleration vector of the pedestrian to identify the orientation of the IMU. The active gyroscopic data axis for the pedometer algorithm is chosen based on the orientation.
Embargo
Oxygen: A Distributed Health Care Framework for Patient Health Record Management and Pharmaceutical Diagnosis
(IEEE, 2022-12-09) Wickramarathna, M; De Silva, K; Lekamalage, V; Senanayake, J; Perera, J; Ruggahakotuwa, L
With the COVID-19 pandemic, the world is confronting various healthcare issues, and healthcare automation is more crucial than ever. The pandemic has revealed the limitations of existing digital healthcare systems to manage public health emergencies. There is no registered population for many healthcare institutions in Sri Lanka, as a result, there is a communication gap. Electronic Health Record systems (EHRs) are becoming popular to share patient details but accessing scattered data across several EHRs while safeguarding patient privacy remains a challenge. Most of these medical records are in printed format and manually entering those into EHR systems is time-consuming and error prone. Not only that pharmaceutical error is a critical healthcare problem, but it is even riskier to visit doctors for pharmaceutical diagnosis during a pandemic. This research introduces a Blockchain-based patient health record system, an Optical Character Recognition (OCR) and Natural Language Processing (NLP) based Medical Document Scanner, a Drug Identifier based on Image Processing and a Medical Chatbot powered by NLP as four novel approaches to address these issues. Altogether with the results, this research aims at introducing a solution for the limitations in healthcare while providing a distributed healthcare framework for the healthcare community worldwide.
Open Access
The Properties of Lime/Soil Concrete
(SLIIT, 2022-02-11) Perera, J; Chandrasiri, J
The investigation of materials for replacing cement in concrete manufacturing has garnered steady interest from experts in recent years. However, the majority of past researches have only focused on the use of lime as a cement substitute in producing Lime Concrete. The reason for this is that lime concrete can be made easily and cheaply while still providing a durable material that can minimize negative environmental impacts. Even though lime is used as an alternative material the integration of a new material as a replacement for conventional aggregates has been limited. As a result, this study will attempt to examine the various compositions of hydraulic lime as a partial replacement of cement while completely replacing the coarse and fine aggregate with a soil to find the influence on the physical characteristics of Lime/Soil concrete. This will also help in decreasing the ecological imbalance caused due to the excess use of conventional aggregates. Locally available reddish-brown laterite soil was used in this study without any modifications. C30 concrete mixes containing 0%, 10%, 15% of hydraulic lime replaced with OPC and complete replacement of aggregate with laterite soil were casted before subjected to water curing. Workability, compressive strength, splitting tensile strength and water absorption test were conducted in accordance with the existing standard. Based on the results obtained from the study it has shown that even with complete replacement of aggregate with laterite soil it was able to produce workable concrete with satisfactory strength that can be employed for ground improvements in pavement design and to manufacture economical non-load bearing concrete blocks. The targeted strength still can be achieved with replacement of 15% hydraulic lime for a lower cost. With the accomplishment from the composition, future studies will be able to better assess the long-term effects of construction operations on the environment.
Embargo
Task and Process Capturing Toolkit using GUI Automation
(IEEE, 2022-06-27) Perera, R. L; Bellanthudawa, H. P; Hevavitharana, N. D; Ariyasinghe, K. M; Wickramarathne, J; Perera, J
Automation is one of the best ways to make easier everyone's day-to-day life. When considering the industry, if someone could be able to automate the mechanism of solving computer software issues, that will be helpful to every company. Because most companies are facing difficulties when it comes to giving IT support. Some companies are suffering from a lack of IT supporters because they have to solve the same problem on different computers at the same time. This most likely reduces the efficiency of the company and affects its performance as well. Without the proper knowledge of technology, employees tend to get a lot of technical issues while working with the technology. Here comes the opportunity to automate the IT-related problem-solving mechanism. That will save a lot of time and increase the company's efficiency as well. While carrying out the background research we thought, by automating the IT-related problem it will reduce the allocation of human power and that will directly affect company efficiency. The research team came up with the idea of creating a software product that can export an executable file by capturing selected tasks of the user's screen and its' relevant processes. Task and process capturing toolkit is a software product called the “ClickMe toolkit”. It can capture relevant processes that are happening in any computer which runs on Windows 10 platform, then make a script called “ClickMe script”, and convert it into an executable file. It can be run on any Windows 10 Platform.