• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1334-1337
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• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1034-1038
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• 2005

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.

• Ocean Science Journal
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• v.40 no.3
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• pp.109-117
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• 2005

Daily changes in phytoplankton abundance and species composition were monitored from July to September 2003 (n=47) to understand which factors control the abundance at a station in Jangmok Bay. During the study, the phytoplankton community was mainly composed of small cell diatoms and dinoflagellates, and the dominant genera were Chaetoceros, Nitzschia, Skeletonema and Thalassionema. Phytoplankton abundance varied significantly from $6.40{\times}10^4$ to $1.22{\times}10^7$ cells/l. The initially high level of phytoplankton abundance was dominated by diatoms, but replacement by dinoflagellates started when the NIP ratio decreased to <5.0. On the basis of the N/P and Si/N ratios, the sampling periofd could be divided into two: an inorganic silicate limitation period (ISLP, $14^{th}$ $July-12^{th}$ of August) and an inorganic nitrogen limitation period (INLP, $13^{th}$ of August - the end of the study). Phosphate might not limit the growth of phytoplankton assemblages in the bay during the study period. This study suggests that phytoplankton abundance and species composition might be affected by the concentrations of inorganic nutrients (N and Si), and provides baseline information for further studies on plankton dynamics in Jangmok Bay.