Tsung-Ying Sun

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The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is(More)
Lane-marking detection is one of the major concerned topics in the field of driving safety and intelligent vehicle. In this paper, a new method using HSI color model for lane-marking detection, HSILMD, is proposed. In HSILMD, full color images are converted into HSI color representation, within the region of interest (ROI) aiming to detect road surface on(More)
As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for particle swarm optimizer(More)
A difficult blind source separation (BSS) issue dealing with an unknown and dynamic number of sources is tackled in this study. In the past, the majority of BSS algorithms familiarize themselves with situations where the numbers of sources are given, because the settings for the dimensions of the algorithm are dependent on this information. However, such an(More)
This paper presents a floorplanning method based on particle swarm optimization (PSO). We adopted the B*-tree floorplan structure to generate an initial stage with overlap free for placement and utilized PSO to find out the potential optimal placement solution. Unlike other related research, our method can avoid the solution from falling into the local(More)
In this paper, a sharing evolution genetic algorithms (SEGA) is proposed to solve various global numerical optimization problems. The SEGA employs a proposed population manager to preserve chromosomes which are superior and to eliminate those which are worse. The population manager also incorporates additional potential chromosomes to assist the solution(More)
The purpose of this paper is to develop an intelligent maneuvering decision system (IMDS) for computer generated forces (CGF). The proposed CGF can take actions similar to a human pilot to gain an advantageous status over the enemy target using the IMDS. The IMDS will produce the best control command from the control alternatives for the CGF in an air(More)