Research on the Orthogonal Fundamental Mode Fluxgate Sensor Circuit

Yuan, Zhenzhong; Zhang, Yuxin; Wang, Dong; Jiang, Yingdan; Guo, Ronghai

doi:10.1109/access.2020.3016783

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Next, the channel weights are generated by fast 1D convolution with a kernel size of K. Finally, the weights are multiplied by the feature map, as shown in Figure 3b. Where K represents the coverage of local cross-channel interactions, which is adaptively determined by the channel dimension C. The formula is as in Equation (1). where |t| odd denotes the closest odd number of t. 𝛾 is set to 2 and b is set to 1.…”

Section: Efficient Channel Attention-c3mentioning

confidence: 99%

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Aluminum Surface Defect Detection Algorithm Based on Improved YOLOv5

Liang,

Kong,

et al. 2023

Advcd Theory and Sims

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Industrial defect detection is an important aspect of object detection. Aluminum is an indispensable material in the industrial field, but the complexity of surface defects on aluminum makes detection challenging. Therefore, the paper proposes the YOLOv5‐ESP algorithm based on the YOLOv5 algorithm. First, the problem of poor signal quality and small data samples is addressed through data enhancement. Second, the YOLOv5‐ESP incorporates the Efficient Channel Attention‐C3 (ECA‐C3) module in the Backbone structure to enhance attention toward defect regions. The Spatial Pooling Pyramid Cross‐Stage Partial Convolution (SPPCSPC) module is introduced to extract multi‐scale features of defects. The Poly‐scale Convolution (PSConv) is applied at the end of the Neck structure to resolve the problem of imprecise localization resulting from significant differences in the scale of defect features. Soft Non‐Maximum Suppression (Soft‐NMS) is utilized in the Head structure to optimize the prediction boxes. Experimental results show that the proposed YOLOv5‐ESP achieves a mean Average Precision (mAP) value of 94%, outperforming YOLOv5 and other classical algorithms. Furthermore, it maintains an average recognition time of 0.019 s per image, which meets the requirements of accuracy and real‐time performance in the industry.

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Section: Efficient Channel Attention-c3mentioning

confidence: 99%

“…Current defect detection methods can be broadly categorized into manual‐based, machine device‐based, and machine vision‐based approaches. [ 1 ] Defect detection methods based on manual is to classify and locate defective images manually. This type of method has the problem of a low detection rate and a high missed detection rate.…”

Section: Introductionmentioning

confidence: 99%

Aluminum Surface Defect Detection Algorithm Based on Improved YOLOv5

Liang,

Kong,

et al. 2023

Advcd Theory and Sims

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Section: Parallel Fluxgate Sensorsmentioning

confidence: 99%

Fabrication Advancements in Integrated Fluxgate Sensors: A Mini Review

2022

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Research on magnetic sensors has been carried out in recent years to enhance sensing capability, enabling us to understand the Earth's magnetic field changes [1] with better sensitivities of 10 À10 to 10 À2 T. [2] Magnetic field sensors have been extensively investigated according to their operating principles and actions in various applications. Sensitivity and selectivity are the two essential properties crucial in the evolution of magnetic field sensors to achieve high fidelity outputs. The former is strengthened by increasing the sensor area and improvements to the planar surface with various Nano-materials. In contrast, the latter is improved by using multiple materials and techniques to enhance interfacial interactions.Magnetic sensors detect disturbances and changes in magnetic fields such as force, direction, and flux. It is a transducer that converts a magnetic field into an electrical voltage. There are several types of magnetic field sensors, each with its own sensitivity range. In addition to sensitivity, the temperature range, the physical size of the sensor, and its on-chip manufacturability must all be considered. Magnetic fields of less than 100 pT are extremely weak and far below the earth's magnetic field. Earth magnetic field sensors have a sensitivity range of 10 μT to a few micro-Tesla, while bias magnet field (high field) sensors have a sensitivity of more than 1 mT.Magnetic fields of less than 100 pT are extremely weak and far below the earth's magnetic field. Earth magnetic field sensors have a sensitivity range of 10 μT to a few micro-Tesla, while bias magnet field (high field) sensors have a sensitivity of more than 1 mT. The most sensitive low-field sensor is the superconducting quantum interference device (SQUID). It is based on Brian J. Josephson's research on a point-contact connection for measuring extremely low currents.The SQUID ring, the radio-frequency coil, and the big antenna loop are all superconducting components of the SQUID sensor. All three must be cooled to the point where they become superconducting. The SQUID itself is small, but the requirement for liquid helium cooling makes the entire apparatus big and hefty. Magnetoresistive sensors (MR) have a high sensitivity, are inexpensive to manufacture, and are small in size. However, they have poor noise performance and linearity, as well as a small dynamic range, making them unappealing and unsuitable for many low-noise applications. Furthermore, as the power usage is reduced, their sensitivity degrades.Search-coil or induction sensors are high-sensitivity, lowpower sensors, but their sensitivity decreases as the cross-section area decreases, making miniaturization ineffective. Because search coils can detect an alternating current magnetic field, they cannot detect a static magnetic field like the Earth's magnetic field. Magnetic sensors based on semiconductors, such as Hall-Effect sensors, magnetotransistors, and magnetodiodes, are very small in size. Because of its low manufacturing cost

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“…This brings excellent magnetic properties, such as low coercivity (tens of A/m [ 2 , 3 ]) and vanishing magnetostriction (

) [ 4 , 5 ]. These characteristics make Cobalt-rich wires ideal for different magnetic sensors [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Among them, orthogonal fluxgates in fundamental mode [ 13 ] have shown a remarkable low noise due to the mechanism that keeps their magnetization always in a saturated state, reducing the Barkhause noise.…”

Section: Introductionmentioning

confidence: 99%

An Improved Composition of CoFeSiB Alloy for Orthogonal Fluxgates

Butta

Janošek

Pařez

et al. 2022

Sensors

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Orthogonal fluxgates in fundamental mode based on (Co0.94Fe0.06)72.5Si12.5B15 cores have recorded very low noise in literature, especially if Joule annealing is performed on the core for a short period of time. However, for annealing time longer than 20–30 min, the noise of the sensor has a tendency to increase. In this work, we investigated this phenomenon, and we have found its origin in a monotonic increase of magnetostriction during the annealing process. We show that the wires with vanishing magnetostriction in their as-cast form exhibit positive magnetostriction after long-time annealing (more than 30 min), which increases the noise of the sensor. After researching the effect of the magnetostriction after annealing on the noise, we propose an alloy with a reduced amount of iron. Less iron leads to a larger as-cast negative magnetostriction, which becomes almost zero after long-time annealing (60 min), bringing further reduction of noise. We prove this effect on two wires from two different manufacturers, although with the same composition. The noise decrease with prolonged annealing is mainly observable in the low-frequency region: at 100 mHz, the noise of a single-wire sensor decreased from 20pT/Hz to 6pT/Hz when the annealing time was prolonged from 10 to 60 min.

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Research on the Orthogonal Fundamental Mode Fluxgate Sensor Circuit

Cited by 6 publications

References 22 publications

Aluminum Surface Defect Detection Algorithm Based on Improved YOLOv5

Aluminum Surface Defect Detection Algorithm Based on Improved YOLOv5

Fabrication Advancements in Integrated Fluxgate Sensors: A Mini Review

An Improved Composition of CoFeSiB Alloy for Orthogonal Fluxgates

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