A context switchable fuzzy inference chip

Cao, Qi; Lim, Meng‐Hiot; Li, Ju Hui; Ong, Yew-Soon; Ng, Willie

doi:10.1109/tfuzz.2006.876735

Cited by 27 publications

(1 citation statement)

References 34 publications

(45 reference statements)

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“…The information provided in this paper is significant for the planning of an air reconnaissance mission. However the procedure has limitations and is sensitive to some of the input parameters, leaving room for improvements in future research based on computational intelligence approaches [25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Contour Based Path Planning with B-Spline Trajectory Generation for Unmanned Aerial Vehicles (UAVs) over Hostile Terrain

Kan¹,

Lim²,

Yeo³

et al. 2011

JILSA

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This research focuses on trajectory generation algorithms that take into account the stealthiness of autonomous UAVs; generating stealthy paths through a region laden with enemy radars. The algorithm is employed to estimate the risk cost of the navigational space and generate an optimized path based on the user-specified threshold altitude value. Thus the generated path is represented with a set of low-radar risk waypoints being the coordinates of its control points. The radar-aware path planner is then approximated using cubic B-splines by considering the least radar risk to the destination. Simulated results are presented, illustrating the potential benefits of such algorithms

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Section: Discussionmentioning

confidence: 99%

Contour Based Path Planning with B-Spline Trajectory Generation for Unmanned Aerial Vehicles (UAVs) over Hostile Terrain

Kan¹,

Lim²,

Yeo³

et al. 2011

JILSA

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Multi-agent Control System with Intelligent Optimization for Building Energy Management

Kan

Yadanar

Ling

et al. 2015

Proceedings in Adaptation, Learning and Optimization

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Abstract. Smart, sustainable and energy-efficient buildings have recently become a trend for intelligent and green building industry. One of the challenges for such a building is to minimize power consumption without compromising the occupant comfort. It is possible to sustain high comfort level with minimal energy consumption through intelligent multi-agent control and optimization. Our work focuses on a new algorithmic aspect of a multi-agent control system that makes buildings more intelligent, resource efficient and comfortable for occupants. A multi-agent control system with intelligent optimization is proposed to optimize HVAC (heating, ventilation and airconditioning) processes using computational intelligence approaches. The optimum solutions generated through the optimization engine and task scheduling for each agent will be used to sustain a high level of occupant comfort with minimal power consumption, increase energy utilization efficiency, and consequently reduce energy cost. Experimental results and comparisons with other evolutionary approaches demonstrate the overall performance and potential benefits of the new framework.

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