Browsing by Author "Walgama, K. S"

Now showing 1 - 4 of 4

Open Access
Dempster–Shafer information filtering framework: Temporal and spatio-temporal evidence filtering
(IEEE, 2015-06-04) Weeraddana, D. M; Kulasekere, E. C; Walgama, K. S
This paper presents an information processing framework for distributed sensor networks. The framework is capable of directly processing temporally and spatially distributed multimodality sensor data to extract information buried in the noise clutter. Moreover, we introduce distributed algorithms to implement spatio-temporal filtering applications in grid sensor networks within the context of the framework. The proposed framework is based on the belief notions in Dempster-Shafer (DS) evidence theory and evidence filtering method. Further analysis is done by exploiting a fire propagation scenario when high noise is present in the sensed data. We compare intuitively appealing results against DS fusion method to grant further credence to the proposed framework.
Embargo
Dempster–Shafer information filtering framework: Temporal and spatio-temporal evidence filtering
(IEEE, 2015-06-04) Weeraddana, D. M; Kulasekere, E. C; Walgama, K. S
This paper presents an information processing framework for distributed sensor networks. The framework is capable of directly processing temporally and spatially distributed multimodality sensor data to extract information buried in the noise clutter. Moreover, we introduce distributed algorithms to implement spatio-temporal filtering applications in grid sensor networks within the context of the framework. The proposed framework is based on the belief notions in Dempster-Shafer (DS) evidence theory and evidence filtering method. Further analysis is done by exploiting a fire propagation scenario when high noise is present in the sensed data. We compare intuitively appealing results against DS fusion method to grant further credence to the proposed framework.
Open Access
Dempster-Shafer Information Filtering in Multi-Modality Wireless Sensor Networks
(publications.waset.org, 2013-07-27) Weeraddana, D. M; Walgama, K. S; Kulasekere, E. C
A framework to estimate the state of dynamically varying environment where data are generated from heterogeneous sources possessing partial knowledge about the environment is presented. This is entirely derived within Dempster-Shafer and Evidence Filtering frameworks. The belief about the current state is expressed as belief and plausibility functions. An addition to Single Input Single Output Evidence Filter, Multiple Input Single Output Evidence Filtering approach is introduced. Variety of applications such as situational estimation of an emergency environment can be developed within the framework successfully. Fire propagation scenario is used to justify the proposed framework, simulation results are presented.
Embargo
Optimal placement of actuators and sensors for floor vibration control
(American Society of Civil Engineers, 2000-12) Hanagan, L. M; Kulasekere, E. C; Walgama, K. S; Premaratne, K
Lightweight floor systems are susceptible to excessive levels of vibration caused by occupant activities such as walking, dancing, and aerobics. Ongoing research utilizes active control to reduce vibration. Placement of multiple actuators and sensors for effective vibration control of flexible systems such as floors is a challenging task. The method proposed in this paper simultaneously determines optimal placements for multiple actuators, sensors, and appropriate output feedback gains of the controller. Instead of carrying out the controller design in the analog domain and then applying a transformation to arrive at a digital implementation, the proposed algorithm provides a digital controller directly. This allows lower sampling rates to be used at the implementation stage. To obtain faster settling times in the presence of external disturbances, a performance index that penalizes system states exponentially is utilized. The nonlinearities associated with actuator saturation due to force/stroke limitations is considered explicitly in the optimization. The proposed algorithm uses an interpolation scheme in case only partial knowledge of the mode shape is available. Hence it is applicable to a wider class of 2D structures that do not possess closed-form expressions for modal shapes and for which one has to resort to finite-element or experimental modal analysis.