Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Open Access Experimental study on an efficient dengue disease management system: Planning and optimizing hospital staff allocation(The Science and Information (SAI) Organization, 2018-01) Jayasuriya, M. M. C; Galappaththi, G. K. K. T; Sampath, M. A. D; Nipunika, H. N; Rankothge, WDengue has become a serious health hazard in Sri Lanka with the increasing cases and loss of human lives. It is necessary to develop an efficient dengue disease management system which could predict the dengue outbreaks, plan the countermeasures accordingly and allocate resources for the countermeasures. We have proposed a platform for Dengue disease management with following modules: (1) a prediction module to predict the dengue outbreak and (2) an optimization algorithm module to optimize hospital staff according to the predictions made on future dengue patient counts. This paper focuses on the optimization algorithm module. It has been developed based on two approaches: (1) Genetic Algorithm (GA) and (2) Iterated Local Search (ILS). We are presenting the performances of our optimization algorithm module with a comparison of the two approaches. Our results show that the GA approach is much more efficient and faster than the ILS approach.Publication Embargo Towards a Smart City: Application of Optimization for a Smart Transportation Management System(IEEE, 2018-12) Thiranjaya, C; Rushan, R; Udayanga, P; Kaushalya, U; Rankothge, WIntelligent traffic planning, the efficiency of public transport and the improved connectivity of all road users in a city, comprise the mobility characteristics of a smart city. In the era of smart cities, efficient and well managed public transportation systems play a crucial role. The planning and allocation of public transportation systems, especially the public bus scheduling is one of the major resource allocation problems where the optimal resource allocation increases the passenger's as well as bus owner's satisfaction. In this research, we have proposed a platform for public transportation management, especially for optimal planning and scheduling of buses. We have used two approaches for our algorithms: Iterated Local Search (ILS) and Genetic Algorithm (GA). In this paper, we are presenting our optimization algorithms and their performances. Our results show that, using our algorithms, we can decide the optimal allocations of buses and plan the bus schedules dynamically in the order of seconds.Publication Embargo Optimization of Volume & Brightness of Android Smartphone through Clustering & Reinforcement Learning (“RE-IN”)(IEEE, 2018-12-21) Abeywardhane, J. S. D. M. D. S; de Silva, E. M. W. N; Gallanga, I. G. A. G. S; Rathnayake, L. N; Wickramaratne, C. J; Sriyaratna, DSmartphone has become one of the most significant piece of technology that humans were able to produce in the 21st century. It has become our life companion; hence the features of the smartphones have developed in advance. But, some features may not work as expected. For instance, auto brightness changing feature is now actualized with smartphones, yet we alter the brightness according to our preference. In the same manner, considering the volume of our smartphone it doesn't change according to our preference subsequently. This research will develop a mobile application (“RE-IN”) to overcome this issue for Android smartphones. Since android smartphones allow accessing its hardware layer we can roll out improvements as we need, yet Apple doesn't permit to proceed with its hardware layer thus hard to do this for the iPhone users. By utilizing the RE-IN mobile application users may have to encounter an optimal brightness and volume on their Android smartphones agreeing the present condition of smartphone users are in. RE- IN application will keep running as a background application on an Android smartphone. When the client changes the brightness and volume as his/her preference. At that point, the reinforcement learning algorithm over the time application will distinguish how to control user's smartphone's brightness and volume relying upon the user's circumstance. When client surrounding is loaded with light, the framework will modify brightness for his/her preference. The client doesn't need to do this manually. Moreover when the client is at the too much boisterous place all of a sudden gets a call from someone; client's smartphone amplifier volume will change consequently and solaces the client's discussion. To actualize this framework it is relied upon to reinforcement learning and machine learning as the research area. By finishing the literature review, research group unable to find an Android mobile application which automates the process of volume and brightness of the Android smartphone as per user preference. After using the reinforcement learning algorithm to learn the data set then distribute the process, using client-server model and come up with a clustering algorithm(K-means algorithm) to share common attributes by considering geographical area which they live in and variables like age, gender, how they interact with the device etc. In addition, this system will identify abnormal behaviors of some particular users. RE-IN will identify the users who are keeping volume level to the highest and brightness level to its maximum and notify them in advance.Publication Embargo Sustainable tourism: Application of optimization algorithms to schedule tour plans(IEEE, 2019-01-31) Perera, D; Rathnayaka, C; Siriweera, L; Dilan, S; Rankothge, WOne of the challenging problems in the tourism industry is to maintain the environmental sustainability of the tourists attracted locations while giving a better user experience for the tourists. The proposed platform for sustainable tourism management system consist with following modules: A prediction module to predict an approximate value on tourist arrival for each location, an optimization algorithm module to decide the number of tourists that can be accommodated in each location considering the environmental sustainability, and an optimal path generating module to show the best route to each location. The optimization algorithm module is developed to decide the number of tourists for each location based on two approaches: Genetic Algorithms and Iterated Local Search. Next the optimal path generating module is developed based on traveling salesman problem.In this paper, the performances of the optimization algorithm module and the optimal path generating module is presented. Results show that, using the suggestions given by the algorithms help the tourist to enjoy a better experience in travelling while ensuring the sustainability in the tourism industry.Publication Embargo Optimization of Customer-Friendly Manual Load Shedding System(IEEE, 2019-12-05) Fernando, W. D. I; Rankothge, W; Perera, A. D. S; Dissanayake, S. J; De Silva, W. D. STo maintain the supply and demand of electricity power, load-Shedding is one of the methods practiced by the energy suppliers to hold the power system balanced, when an energy deficit problem arises. Lacking a proper load shedding scheme will lead to system instability and it will cause serious system frequency decay. We have proposed a solution to optimize the manual load shedding schedule with the application of optimization techniques, specifically the Genetic Algorithms. We have considered current hold by all feeders throughout the country, and the time period of load shedding as main factors in the optimization model. Our results show that, using our proposed model, we can minimize the imbalance between the supply and demand of electricity by selecting the best feeder to be selected for load shedding under given constraints.Publication Embargo Optimization of controller gains for FPGA-based multivariable motion controller using response surface methodology(IEEE, 2015-05-03) Sekaran, H. P; Liyanage, M. H; Krouglicof, NField Programmable Gate Arrays (FPGA) have become increasingly popular in recent years for control applications. Using contemporary FPGA technology, a powerful virtual processor can be synthesized and integrated with custom hardware to create a dedicated controller that outperforms conventional microcontroller and microprocessor based designs. The FPGA based controller takes advantage of both hardware features and virtual processor technology. This study details the development of a cascaded type PD controller for an inverted pendulum system implemented on a single FPGA device. The controller includes a hardware based implementation of the IO modules including quadrature decoders/counters and a Pulse Width Modulation (PWM) controller for the motor driver. The NIOS II processor was synthesized to implement the cascaded PID controller algorithm. This study also proposes a novel method for obtaining the optimal controller gains for the system. It uses the Central Composite Design (CCD) in Response Surface Methodology (RSM) for obtaining these gains. A classic inverted pendulum system was selected to demonstrate the applicability of the proposed approach. The gains provided by the RSM were verified experimentally to validate the proposed controller tuning method.Publication Embargo Optimization of cluster head rotation in energy constrained wireless sensor networks(IEEE, 2007-07-02) Gamwarige, S; Kulasekere, E. CThe performance of energy driven cluster head (CH) rotation algorithms have been shown to be far more superior to time driven CH rotation algorithms when it comes to maximizing the sensor bed lifetime. The sensor bed life time is improved by selecting the proper points at which a CH role is relinquished to higher energy nodes via a CH rotation phase. However no formal analytical method is available to find the optimal point at which the CH rotation should be carried out. This research proposes an analytical iterative method to obtain the optimal points at which the CH rotation can be carried out. The method proposes an optimal value c opt for the energy threshold at which this rotation could occur. The values obtained via the analytical method is shown to be optimal via simulations using the EDCR and EDCR-MH algorithms. The analytical method proposed can be used in any energy driven algorithm to find the optimal point for CH rotations avoiding any ad hoc simulation based methods to maximize the lifetime of the sensor bed.Publication Open Access Optimization Methodologies for Building Performance Modelling and Optimization(FACULTY OF ENGINEERING, UNIVERSITY OF MORATUWA, 2013) Bandara, R. M. P. S; Attalage, R. ABuildings account for approximately 40% of the global energy consumption and 36% of total carbon dioxide emissions. At present, high emphasis is given on the reduction of energy consumption and carbon footprint by optimizing the performance and resource utilization of buildings to achieve sustainable development. Building performance is analyzed in terms of energy performance, indoor environment for human comfort & health, environmental degradation and economic aspects. As for the energy performance analysis, this can be best modeled and optimized by a whole building energy simulation tool coupled with an appropriate optimization algorithm. Building performance optimization problems are inherently multivariate and multi-criteria. Optimization methodologies with different characteristics that are broadly classified as Adaptive, Non-adaptive and Pareto Algorithms can be applied in this regard. The paper discusses the applicability of the aforementioned optimization methodologies in building performance optimization for achieving realistic results.Publication Embargo Optimum sizing and tracking of combined cooling heating and power systems for bulk energy consumers(Elsevier, 2014-04-01) Jayasekara, S; Halgamuge, Saman K; Attalage, R. A; Rajarathne, RThe optimization of combined cooling heating and power (CCHP) systems involves two major tasks: searching for optimum design parameters and for optimum regular operation variables. This paper proposes a two-stage method to solve both tasks. The operation of large thermal power plants must be altered smoothly, as quick changes in system settings may result in cascade tripping of subsystems, ultimately leading to a complete shutdown. This work uses graphical representation of the operational space of the system, which helps in tracking the operation along its optimum trajectory smoothly. The daily energy demands of a five star hotel, collected over a year, were used to demonstrate the applicability of the proposed method. Using the proposed method reduced the total annual cost over 7% and 13% in Australia and Sri Lanka respectively, compared to the conventional method of following thermal load.