A broad linear range of ionic flexible sensors (IFSs) with high sensitivity is vital to guarantee accurate pressure acquisition and simplify back-end circuits. However, the issue that sensitivity gradually decreases as the applied pressure increases hinders the linearity over the whole working range and limits its wide-ranging application. Herein, we design a two-scale random microstructure ionic gel film with rich porosity and a rough surface. It increases the buffer space during compression, enabling the stress deformation to be more uniform, which makes sure that the sensitivity maintains steady as the pressure loading. In addition, we develop electrodes with multilayer graphene produced by a roll-to-roll process, utilizing its large interlayer spacing and ionaccessible surface area. It benefits the migration and diffusion of ions inside the electrolyte, which increases the unit area capacitance and sensitivity, respectively. The IFS shows ultra-high linearity and a linear range (correlation coefficient ∼ 0.9931) over 0−1 MPa, an excellent sensitivity (∼12.8 kPa −1 ), a fast response and relaxation time (∼20 and ∼30 ms, respectively), a low detection limit (∼2.5 Pa), and outstanding mechanical stability. This work offers an available path to achieve wide-range linear response, which has potential applications for attaching to soft robots, followed with sensing slight disturbances induced by ships or submersibles.
The traditional geomagnetic matching navigation method is based on the correlation criteria operations between measurement sequences and a geomagnetic map. However, when the gradient of the geomagnetic field is small, there are multiple similar data in the geomagnetic database to the measurement value, which means the correlation-based matching method fails. Based on the idea of pattern recognition, this paper constructs a two-stage neural network by cascading a probabilistic neural network and a non-fully connected neural network to, respectively, classify geomagnetic vectors and their feature information in two steps: “coarse screening” and “fine screening”. The effectiveness and accuracy of the geomagnetic vector navigation algorithm based on the two-stage neural network are verified through simulation and experiments. In simulation, it is verified that when the geomagnetic average gradient is 5 nT/km, the traditional geomagnetic matching method fails, while the positioning accuracy based on the proposed method is 40.17 m, and the matching success rate also reaches 98.13%. Further, in flight experiments, under an average gradient of 11 nT/km, the positioning error based on the proposed method is 39.01 m, and the matching success rate also reaches 99.42%.
The nonlinear stochastic resonance (SR) system possesses the ability of taking advantage of noise to enhance the weak signal when the SR system, signal and noise reach to the matching relation. It provides an effective approach to detect the weak magnetic anomaly signal in low signal-to-noise ratio. However, in practical applications, the measured magnetic anomaly signal may be a peak signal, a trough signal, or a combination of the two due to the uncertainty of magnetic target orientation. Hence it is difficult to maintain a good detection performance with single SR system because the SR system output is directly influenced by signal waveforms. Aiming to this, a new strategy using the parallel monostable SR (PMSR) system is proposed, which can ensure the good detection performance regardless of a peak signal, a trough signal, or a combination of the two. Besides, we take the kurtosis index as the criterion and search the optimal system parameters in SR system. The simulation and experiment results indicate its availability, validity and that it can achieve a good detection performance in different waveforms. It can be expected to be widely used in the field of magnetic anomaly detection with PMSR system. INDEX TERMS Magnetic anomaly detection, parallel monostable stochastic resonance, signal waveforms.
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