A compact magnetic resonance system with inside-out sensor for soil moisture measurement

Wan, Cai; Wang, He; Guo, Ping; Wu, J.; He, Yucheng; Xu, Zheng

doi:10.1088/1361-6501/ac6000

Cited by 8 publications

(3 citation statements)

References 53 publications

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“…Furthermore, power electronic components present various stress characteristics under environmental stress [35] (such as temperature and humidity), which can also be considered and weighed to modify threshold values,…”

Section: Three-dimensional Health Monitoring Vectormentioning

confidence: 99%

An online comprehensive health monitoring system for automotive auxiliary converter

Wu,

Xia,

Cui

et al. 2024

Meas. Sci. Technol.

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As an essential part of the electronic system in electric vehicles (EVs), the automotive auxiliary converter (AAC) connects the batteries and burdens the auxiliary power supply. This paper mainly proposes an online comprehensive health monitoring system for AAC. Firstly, based on the control diagram of the LLC resonant converter, three critical components are emerging, including capacitor, optocoupler, and semiconductor. These components can partly reflect the operation condition of the output loop, feedback loop, and control link, respectively. Then the characteristics parameters of these three components, equivalent series resistance (ESR) of the capacitor, current transfer ratio (CTR) of the optocoupler, and on-resistance of the semiconductor jointly construct the three-dimensional health monitoring vector. Finally, the online monitoring accuracy is verified and the proposed system can thus effectively promote the operation and maintenance of AAC. Furthermore, the online health monitoring system enables a flexible expansion, which can be further customized into different kinds of power converters, showing high applicability and practicality.

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Section: Three-dimensional Health Monitoring Vectormentioning

confidence: 99%

An online comprehensive health monitoring system for automotive auxiliary converter

Wu,

Xia,

Cui

et al. 2024

Meas. Sci. Technol.

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“…These parts are connected in series through water pipes to form a complete water circuit Figure 7 is the overall circuit structure diagram of the circuit section. When the magnetic sensing probe reaches the magnetic field to be measured, the spectrometer [39,40] outputs a low-power RF signal corresponding to the Larmor frequency of the hydrogen atomic nucleus that is proportional to the strength of the magnetic field to be measured. After amplification by an RF power amplifier, the duplexers isolate the high-power RF signal and directly transmit it to the tuning and matching network of the probe, generating an RF field perpendicular to the magnetic field direction of the coil and causing the macroscopic magnetic angle deviation of the nucleus to demonstrate a nuclear magnetic resonance phenomenon.…”

Section: System Designmentioning

confidence: 99%

NMR Magnetometer Based on Dynamic Nuclear-Polarization for Low-Strength Magnetic Field Measurement

Guo

Wan

et al. 2023

Sensors

Self Cite

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Nuclear magnetic resonance (NMR) magnetometers are considered due to their ability to map magnetic fields with high precision and calibrate other magnetic field measurement devices. However, the low signal-to-noise ratio of low-strength magnetic fields limits the precision when measuring magnetic fields below 40 mT. Therefore, we developed a new NMR magnetometer that combines the dynamic nuclear polarization (DNP) technique with pulsed NMR. The dynamic pre-polarization technique enhances the SNR under a low magnetic field. Pulsed NMR was used in conjunction with DNP to improve measurement accuracy and speed. The efficacy of this approach was validated through simulation and analysis of the measurement process. Next, a complete set of equipment was constructed, and we successfully measured magnetic fields of 30 mT and 8 mT with an accuracy of only 0.5 Hz (11 nT) at 30 mT (0.4 ppm) and 1 Hz (22 nT) at 8mT (3 ppm).

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“…Soil organic matter is a key parameter to measure the degree of land degradation and soil fertility, and plays an important role * Author to whom any correspondence should be addressed. in sustainable agricultural development and environmental protection [1][2][3][4][5][6]. Organic matter not only improves soil microbial activity, but also provides crops with essential nutrients for growth [7,8].…”

Section: Introductionmentioning

confidence: 99%

Development of a vehicle-mounted soil organic matter detection system based on near-infrared spectroscopy and image information fusion

Cao,

Yang,

et al. 2024

Meas. Sci. Technol.

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In the practical application of farmland, the soil organic matter prediction model es-tablished by the traditional near-infrared spectroscopy is affected by factors such as soil texture, which leads to a serious decline in the accuracy of the model. To im-prove the robustness and prediction accuracy of the model, a prediction model based on near-infrared spectroscopy and image fusion is proposed. A 1D CNN organic matter prediction model (based on near-infrared spectroscopy) was established using eight characteristic wavelengths of extracted soil organic matter (932 nm, 999 nm, 1083 nm, 1191 nm, 1316 nm, 1356 nm, 1583 nm, and 1626 nm) as spectral infor-mation. A 2D CNN organic matter prediction model was established using soil RGB images as information. Based on the idea of model weight fusion, 1D CNN and 2D CNN models are fused. When using small convolutional kernels(3-layer convolu-tional kernel size: 3 * 3, 1 * 1, 1 * 1)and 1D-CNN: 2D-CNN = 6:4, the model has the highest prediction accuracy(R2=0.872). The optimal fusion model was embedded into the inspection system. The final laboratory and field testing results are as fol-lows: under laboratory conditions, the detection accuracy R2 of the 1D CNN predic-tion model, 2D CNN prediction model, and fusion model are 0.838, 0.781, and 0.869, respectively. The RMSE is 3.005, 3.546, and 2.678, respectively. The above experi-mental data indicates that the R2 of the fused model is more accurate compared to the model established with a single information. In the field test, the R2 detection accuracy of 1D CNN prediction model, 2D CNN prediction model and fusion model is 0.809, 0.731 and 0.835, respectively. The root mean square errors are 3.466, 3.828 and 2.973, respectively. The results show that this testing system can meet the needs of soil nutrient testing in farmland and provide guidance for precision agriculture management.

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A compact magnetic resonance system with inside-out sensor for soil moisture measurement

Cited by 8 publications

References 53 publications

An online comprehensive health monitoring system for automotive auxiliary converter

An online comprehensive health monitoring system for automotive auxiliary converter

NMR Magnetometer Based on Dynamic Nuclear-Polarization for Low-Strength Magnetic Field Measurement

Development of a vehicle-mounted soil organic matter detection system based on near-infrared spectroscopy and image information fusion

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