Browsing by Author "Wickramanayake, SKK"

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item: Conference-Full-text
Applications of game theory in software engineering
(Department of Computer Science and Engineering, University of Moratuwa., 2012) Herath, SWHMSP; Gunarathna, UARR; Hettiarachchi, HAC; Herath, HMAB; Wickramanayake, SKK; De Silva, R
Turning a new chapter in game theory some of its applications to the field of Software Engineering are explored recently. Game theory addresses strategic problems. There are many aspects in software development process which could be analyzed using game theory. This is a discussion and a research of how game and why theory is applied Many real life situations including situations arise in software engineering process can be abstracted into prisoners' dilemma situations. Game theory principal usage in software engineering, technical and non technical aspects of software engineering, project management and avoiding software development failures are discussed in game theory perspective. Maintenance is another major aspect of Software Engineering. Application of strategies of game theory in software maintenance would be beneficial. In that way game theory can be used for the benefit of the software engineering processes because meetings are ubiquitous in software engineering projects.
item: Thesis-Full-text
Modeling and enhancing fuel economy of fleet vehicles based on data analytics
(2018) Wickramanayake, SKK; Bandara, HMND; Samarasekara, N
Fuel consumption of a vehicle depends on several internal factors such as distance, load, vehicle characteristics, and driver behavior, as well as external factors such as road conditions, traffic, and weather. Moreover, not all of these factors are easily obtainable for the fuel consumption analysis. Therefore, fuel-fraud is relatively easier to conceal; thus, considered a significant threat to the fleet industry by managers. This research model and evaluate the fuel consumption of fleet vehicles based on vehicular data and suggest suitable process improvement actions to improve the fuel economy. We first model and predict the fuel consumption to identify possible frauds. We considered a case where only a subset of the factors mentioned above is available as a multivariate time series from a long-distance public bus. An evaluation of several machine learning techniques revealed that Random Forest could predict fuel consumption with 95.9% accuracy. To verify the detected cases of possible fuel fraud, we propose to use different indicators such as speed profile, the frequency of harsh events, total idle time, and day of the week. Further, we propose a solution to promote fuel-efficient driving through real-time monitoring and driver feedback. A classification model, derived from historical data, identifies fuel inefficient driving behaviors in real-time. The model considers both the driver-dependent and environmental parameters such as traffic, road topography, and weather in determining driving efficiency. If an inefficient driving event is detected, a fuzzy logic inference system is used to determine what the driver should do to maintain fuel-efficient driving behavior. The decided action is conveyed to the driver via a smartphone in a nonintrusive manner. We demonstrate that the proposed classification model yields an accuracy of 85.2% while increasing the fuel efficiency up to 16.4%.
item: Conference-Abstract
Riyadisi - Intelligent driver monitoring system
(2014-06-26) Darshana, KUGS; Fernando, MDY; Jayawadena, SS; Wickramanayake, SKK
Road accidents have become a major problem that causes nearly 1.3 million people die and 25-50 million people injured or disabled in each year all around the world. It has been calculated that 10% to 20% of traffic accidents with dead drivers are caused by driver inattention. Drowsiness and distraction of drivers have become the main causes for driver inattention. Drowsiness is something unavoidable and out of control of the driver. Sleepiness increases reaction time and generates the decreased vigilance level, alertness and concentration. Hence the quality of decision making may be affected. Reduced attention and raised reaction time increase the probability of road accidents. Distractions such as looking away, using mobile phone or navigation systems are also obviously cause to make driver loses his/ her focus.