Currently, when the existing magnetic dipole inversion methods are used, the classification process heavily relies on the localization results, and the localization error can significantly deteriorate the classification results. In order to address this problem, the present study proposes a novel magnetic dipole inversion method based on tensor geometric invariants, in which localization and classification processes are mutually independent. First, based on tensor geometric invariants, it was proved that the cross product between the intermediate eigenvectors at any two measurement points in the dipole magnetic field is either in the same direction as the magnetic moment vector or in the opposite direction. Accordingly, the direction of the magnetic moment vector could be directly obtained. Next, based on tensor geometric invariants, nonlinear equations including the position parameters of the dipole were constructed so as to derive the position of the dipole. By employing the proposed method, localization and classification were found to be two mutually independent processes, both of which are relatively insensitive to attitude changes of the measurement system. The present simulation results demonstrate that the proposed method is superior to the scalar triangulation and ranging (STAR) method, the Nara improved method, and the STAR improved method in both classification and localization performance. Moreover, the proposed method exhibits the strongest noise immunity and can be effectively used for real-time inversion.
The circuit system designed in this paper uses FPGA and single-chip microcomputer as the main chips. It uses VHDL language programming and performs functional simulation and verification of the core control program through ModelSim software, and finally, a storage-type dynamic measurement and control system with one-to-multiple channels, adjustable trigger modes, and optional sampling frequency is realized. At the same time, the system is further optimized in terms of power consumption and volume. The storage measurement and control system can successfully record eight-channel dynamic voltage signals, and the relative error of the experimental results is less than 2% through the simulation trigger experiment and Hopkinson bar experiment loading.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.