Jinzhe Wang , Kai Li , Wenhui Jia , Xinqiang Liu , Qin Shi , Xuanqi Wu , Jinbao Li , Jialong Wang , Guang Yang , Na Shi
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Differential optimization measurement technique for magnetic gradient tensors in magnet positioning
The magnetic gradient tensor provides more information about the magnetic source in anomaly detection. However, approximation errors inevitably arise from the differential measurement method. Therefore, a differential optimization measurement method based on sensor positions fine-tuning is proposed in this study. This method introduces offset coefficients to establish a theory-measurement discrepancy model. Also, a minimum-overall-discrepancy optimization strategy is used to guide the hybrid PSO-Dogleg algorithm in parameter searching. Thus, the differential approximation closely estimates the gradient. The numerical simulation shows the proposed method is applicable across various detection regions, magnetic moments, and noise levels. Furthermore, the correlation between tensor measurement errors and positioning performance was analyzed, and positioning experiments were conducted. As a result, the root mean square error of the tensor component is reduced by 7.1 %, while the average positioning error is reduced from 0.244 m to 0.136 m, which effectively improves the positioning accuracy of the magnetic gradient tensor.
期刊介绍:
in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance.
Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.