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학술지 Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion
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저자
김현석, 서동준
발행일
201809
출처
Sensors, v.18 no.9, pp.1-12
ISSN
1424-8220
출판사
MDPI AG
DOI
https://dx.doi.org/10.3390/s18092792
협약과제
18ZH1100, 사물-사람-공간의 유기적 연결을 위한 초연결 공간의 분산 지능 핵심원천 기술, 손영성
초록
A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.
KSP 제안 키워드
Data Fusion Model, Hybrid PSO, Hybrid particle swarm optimization(HPSO), Multi-Sensor Data Fusion, Multi-sensor System, Nonlinear characteristics, Proposed model, Robust data, exploration and exploitation, fusion algorithm, large-scale
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