Search Results (2)

In the source location of underground explosions, the phase non-consistency among sensors can cause significant errors in the extraction of the time difference in the arrival of seismic waves, seriously affecting the accuracy of source location. To address the above-mentioned problem, this paper proposes a phase compensation method based on the Multi-strategy Quantum behaved Particle Swarm Optimization (MQPSO) algorithm. First, this method calibrates the phases of vibration sensors to obtain the phase differences among sensors. Second, it uses the MQPSO intelligent optimization algorithm to correct the phase differences among vibration sensors. Finally, simulations and field tests are carried out for verification. The experimental results show that after adopting the phase compensation method with MQPSO, the range of phase differences in sensors is reduced by an average of 91% compared with the uncompensated state. This fully verifies that the phase compensation method of MQPSO can effectively complete the phase consistency calibration of sensors, providing important support for the source location of underground explosions. Full article

Modern information fusion systems essentially associate decision-making processes with multi-sensor systems. Precise decision-making processes depend upon aggregating useful information extracted from large numbers of messages or large datasets; meanwhile, the distributed multi-sensor systems which employ several geographically separated local sensors are required to provide sufficient messages or data with similar and/or dissimilar characteristics. These kinds of information fusion techniques have been widely investigated and used for implementing several information retrieval systems. However, the results obtained from the information fusion systems vary in different situations and performing intelligent aggregation and fusion of information from a distributed multi-source, multi-sensor network is essentially an optimization problem. A flexible and versatile framework which is able to solve complex global optimization problems is a valuable alternative to traditional information fusion. Furthermore, because of the highly dynamic and volatile nature of the information flow, a swift soft computing technique is imperative to satisfy the demands and challenges. In this paper, a nonlinear aggregation based on the Choquet integral (NACI) model is considered for information fusion systems that include outliers under inherent interaction among feature attributes. The estimation of interaction coefficients for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO) and the high breakdown value estimator, least trimmed squares (LTS). From simulation results, the proposed MQPSO algorithm with LTS (named LTS-MQPSO) readily corrects the deviations caused by outliers and swiftly achieves convergence in estimating the parameters of the proposed NACI model for the information fusion systems with outliers. Full article