To detect the shape of a small magnetic target in the shallow underground layer, this paper proposes a recognition method based on Mask-RCNN. Firstly, using COMSOL software and MATLAB software to establish the database of magnetic targets model under different shapes and orientations, which greatly enriched the diversity of the training data set. Then, the Gzz component of the magnetic gradient tensor matrix is selected to highlight the shape features of the magnetic target, and the contour image is generated. The experimental data set is created by using the deep learning annotation tool Labelme. Finally, Resnet101 is used as the backbone network and feature pyramid network (FPN) structure is used to extract features. The regional recommendation network (RPN) is trained end-to-end to create regional recommendations for each feature map. The detection results of 200 test images show that the average detection accuracy of the method is 97%, and the recall rate is 94%. The simulation results show that the recognition accuracy and robustness of the method are improved.

The mouse Magnetocardiogram’s (MCG) accurate measurement is of the utmost importance for studying heart diseases. However, most of the currently used measuring instruments are Superconducting Quantum Interference Devices (SQUIDs), which are quite problematic. For example, they have a large scale, are expensive, and are difficult to widely promote. To address these issues, we present a mouse MCG measurement system using a Spin-Exchange-Relaxation-Free (SERF) atomic magnetometer. The measurement system mainly consists of a magnetic sensor, a mouse fixation device, a magnetically shielded device, and a data acquisition and processing module. We fabricated a single-axis SERF rubidium atomic magnetometer with a sensitivity of 20 fT/ √ Hz to serve as the system’s magnetic sensor. We also conducted experiments on the mouse MCG signals. Using power line harmonic modeling and wavelet threshold filter processing, we processed the raw signal and obtained an R -peak amplitude of 5 pT; moreover, the P -, QRS -, and T -waves were clearly visible. We also performed heart rate monitoring on mice at different ambient temperatures. Finally, we conducted a comparative analysis of related systems to illustrate the proposed system’s advantages for mouse MCG signals. This work demonstrates that the SERF atomic magnetometer is compatible with the long-term detection needs of the mouse cardiac magnetic field.

Scalar aeromagnetic surveys have played a vital role in prospecting. However, before analysis of the surveys’ aeromagnetic data is possible, the aircraft’s magnetic interference should be removed. The extensively adopted linear model for aeromagnetic compensation is computationally efficient but faces an underfitting problem. On the other hand, the neural model proposed by Williams is more powerful at fitting but always suffers from an overfitting problem. This paper starts off with an analysis of these two models and then proposes a dual estimate method to combine them together to improve accuracy. This method is based on an unscented Kalman filter, but a gradient descent method is implemented over the iteration so that the parameters of the linear model are adjustable during flight. The noise caused by the neural model’s overfitting problem is suppressed by introducing an observation noise.

Based on the core hysteresis features, the RTD-fluxgate core, while working, is repeatedly saturated with excitation field. When the fluxgate simulates, the accurate characteristic model of the core may provide a precise simulation result. As the shape of the ideal hysteresis loop model is fixed, it cannot accurately reflect the actual dynamic changing rules of the hysteresis loop. In order to improve the fluxgate simulation accuracy, a dynamic hysteresis loop model containing the parameters which have actual physical meanings is proposed based on the changing rule of the permeability parameter when the fluxgate is working. Compared with the ideal hysteresis loop model, this model has considered the dynamic features of the hysteresis loop, which makes the simulation results closer to the actual output. In addition, other hysteresis loops of different magnetic materials can be explained utilizing the described model for an example of amorphous magnetic material in this manuscript. The model has been validated by the output response comparison between experiment results and fitting results using the model.

With the continuously increased running speed of high-speed trains, aerodynamic problems become more and more obvious. Running safety of trains under crosswinds is a serious problem among numerous aerodynamic problems. Aiming at this problem, this paper combines computational fluid dynamics with multi-body dynamics to study aerodynamic characteristics and safety problems when the high-speed train is running under crosswinds. Based on fluid software FLUENT, this paper computes the flow field of two high-speed trains passing each other under crosswinds. Meanwhile, with combining the multi-body dynamic software SIMPACK with theories, the paper establishes a multi-body dynamic model of the high-speed train, and applies random aerodynamic loads which act on the high-speed train to this model. Impacts of aerodynamic forces (force moments) on aerodynamic characteristics and running safety of each train compartment in the open air are analyzed. Analyzed results show that: when two high-speed trains are passing each other at the same speed of 350 km/h and the crosswind speed is 15 m/s, maximum pressure fluctuations are at observation points near the lateral windows of the head train; aerodynamic forces and force moments of the head train are more than those of the mid-train and tail-train; the high-speed train still has a high safety performance under crosswinds, where the maximum derailment coefficient is 1.9, the reduction rate of the wheel load is 1.1 and the overturning coefficient is 0.98, all of which are smaller than the limit value. More comprehensive factors are considered by the computational model in this paper. It can be used as an effective method to study and evaluate the performance of two high-speed trains passing each other under crosswinds.

Accurate measurement of magnetic susceptibility plays a pivotal role in diverse scientific disciplines, ranging from material characterization to geophysical exploration. Superconducting Quantum Interference Devices (SQUIDs) have long been regarded as the gold standard for magnetic susceptibility measurements. However, their reliance on cryogenic temperatures and complex setups imposes significant limitations. In this study, we employed a single-axis Spin-Exchange Relaxation-Free (SERF) atomic magnetometer to measure magnetic susceptibility. We propose a new measurement method that resolves the inherent contradiction between the strong magnetic field and the zero-magnetic-field environment required by the SERF state in susceptibility measurements. The article provides a detailed exposition of the instrument’s principles, design, fabrication, and testing results. The achieved measured magnetic moment sensitivity is 2 × 10 ^-6 emu, while the coil system can realize a maximum theoretical sensitivity of 1 × 10 ^-7 emu. Additionally, we conducted tests on actual samples and compared the results with those obtained using SQUIDs. This work demonstrates the potential of SERF atomic magnetometers in achieving low-cost, high-sensitivity measurements of magnetic susceptibility.