As a convenient chemical sensor, pH electrode is widely used in the measurement of pH value of water bodies. However, due to structural aging and environmental influences, pH electrode is prone to drift, which directly results in the inability to obtain accurate measurement results. Based on the above problems, this paper proposes a cascade structure soft compensation model with the Gated Recurrent Unit (GRU) as the main body. The model uses the complete ensemble empirical mode decomposition with adaptive noise with permutation entropy (CEEMDAN-PE) method to obtain the main characteristics of the pH electrode potential drift signal to reduce the interference of noise in the actual measurement environment, and uses its output as the input of the GRU neural network to obtain the prediction result and compensate. This model is called the CEEMDA-PE & GRU (CPG) model. In this paper, the CPG model is compared with the commonly used time series prediction model, and the results show that the prediction effect of this model is better than other models. Root-mean-squared-error (RMSE), mean-absolute-error (MAE), and mean-absolute-percentage- error (MAPE) of the prediction model are reduced by 60.97%, 65.53%, and 66.55% respectively. Finally, this paper proposes the concept of compensation degree to evaluate the compensation effect. The average compensation degree of the soft compensation method is above 83%. It shows that the soft compensation method can improve the measurement accuracy of pH electrode and has good robustness.
In this paper, we design and theoretically investigate a silicon notched disk metasurface-waveguide (NDMW) system, realizing a new approach to excite Fano resonance by introducing a waveguide layer to generate quasi-guided mode (QGMs) resonances. By comparing the transmission characteristics of the silicon complete disk metasurface (CDM), the silicon complete disk metasurface-waveguide (CDMW) system, and the NDMW system, the formation mechanism of Fano resonances in multiple different excitation modes is analyzed. Furthermore, the Fano resonance characteristics of the NDMW system for different polarization conditions are studied. It can be found that different polarization states can excite different multiple Fano resonances in the NDMW system. Finally, the effects of various structural parameters on the multiple Fano resonances characteristics are investigated. Utilizing the narrow linewidth and significant near-field confinement of the Fano resonances, an optical refractive index sensor can be obtained with the sensitivity of 113 nm/RIU and the maximum quality (Q) factor of 2*10^5 . The proposed system opens up an avenue to develop high-performance biological sensors.
Based on the optical properties of waveguide-coupled surface plasmon resonance, a sensor of surface plasmon coupled waveguide resonance with double discrete states generated by the upper and lower waveguide structures combined with prisms and Au films, respectively, is proposed. In this paper, the sensing structure is studied numerically and analytically, and the transmission characteristics of surface equipartition excitations are analyzed and elaborated in-depth in combination with the reflection angle spectrum, as well as the generation and evolution mechanism of dual Fano resonance at different incidence angles at fixed wavelengths. The structural model of Fano resonance sensing based on angle modulation is established, and the sensing characteristics are analyzed to produce electromagnetic field enhancement at low-angle Fano. The mathematical model between structural parameters and spectral performance is established using back propagation (BP) neural network, and the particle swarm optimization (PSO) algorithm is used to find the input structural parameters corresponding to the optimal performance, The results show that the quality factor of resonance (FOM) and sensitivity is significantly improved; When it is used in the field of biosensing, the response curves of Fano with different angles of high and low to concentration are quite different, thus realizing high precision detection of different concentrations of solutions and reflecting excellent sensing performance.
The frequent occurrence of algal blooms has seriously affected the marine environment and human production activities. Therefore, it is crucial to monitor the phytoplankton concentration in water bodies. In this study, a prediction method for brown tide algae using improved Gramian angular field (IGAF) and deep learning based on the laser-induced fluorescence spectrum was proposed. The method combined one-dimensional(1D) fluorescence spectrum with improved Gramian angular field (IGAF) for image coding. The internal normalizing approach of the original Gramian angle field algorithm was upgraded from local to global, which can increase the difference between samples with various concentrations. Then, we established a novel technique that fully takes into account the Gramian angular difference field (GADF) and Gramian angular summation field (GASF) features, allowing it to control the main and sub-diagonal features and successfully convert 1D sequences into images by adding various weight factors. Using depthwise separable convolutional neural network (DSC) to extract image features helps reduce model training parameters, paired with long short-term memory network (LSTM) to rapidly predict the concentration of brown tide. To confirm the actual performance of the given approach, ablation and contrast experiments were carried out, and the results showed that the method's regression accuracy, R2 was 97.8%, with the lowest MSE and MAE. This study investigated the transformation of 1D spectra into images using IGAF, which not only explored the application of the fluorescence spectrum image coding method for algal regression but also enabled the introduction of the potent benefits of deep learning image processing into the field of spectral analysis.
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