BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil.

In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.

Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer.

Breast cancer is the second dangerous cancer in the world. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. This paper proposes the single parameter decision theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We find that when the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the number of 30 attributes of the original breast cancer data dropped to 12, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093 s. The experimental results show that the SPDTRS-PNN model can improve the ac-curacy of breast cancer recognition, reduce the time required for diagnosis.

Selection of characteristic wavelengths using SMA for laser induced fluorescence spectroscopy of power transformer oil.

As the core component of the power system, the accurate analysis of its state and fault type is very important for the maintenance and repair of the transformer. The detection method represented by the transformer oil dissolved gas has the disadvantages of complicated processing steps and high operation requirements. Here, laser induced fluorescence (LIF) spectroscopy was applied for the analysis of transformer oil. Specifically, the slime mould algorithm (SMA) was used to select the characteristic wavelengths of the transformer oil fluorescence spectrum, and on this basis, a transformer fault diagnosis model was constructed. First, samples of transformer oil in different states were collected, and the fluorescence spectrum of the transformer oil was obtained with the help of the LIF acquisition system. Then, different spectral pretreatments were performed on the original fluorescence spectra, and it was found that the pretreatment effect of Savitzky-Golay smoothing (SG) was the best. Then, SMA was used to screen the characteristic wavelengths of the fluorescence spectrum, and 137 characteristic wavelengths were screened out to realize the accurate identification of the fluorescence spectrum of the transformer oil. In addition, the advantages of SMA for feature wavelength screening of transformer oil fluorescence spectra were demonstrated by comparing with traditional feature extraction strategies using principal components analysis (PCA). The research results show that it is effective to use SMA to screen the characteristic wavelengths of the LIF spectroscopy of transformer oil and use it for transformer fault diagnosis, which is of great significance for promoting the development of transformer fault diagnosis technology.

Identification of mine water inrush using laser-induced fluorescence spectroscopy combined with one-dimensional convolutional neural network.

The application of laser-induced fluorescence (LIF) combined with machine learning methods can make up for the shortcomings of traditional hydrochemical methods in the accurate and rapid identification of mine water inrush in coal mines. However, almost all of these methods require preprocessing such as principal component analysis (PCA) or drawing the spectral map as an essential step. Here, we provide our solution for the classification of mine water inrush, in which a one-dimensional convolutional neural network (1D CNN) is trained to automatically identify mine water inrush according to the LIF spectroscopy without the need for preprocessing. First, the architecture and parameters of the model were optimized and the 1D CNN model containing two convolutional blocks was determined to be the best model for the identification of mine water inrush. Then, we evaluated the performance of the 1D CNN model using the LIF spectral dataset of mine water inrush containing 540 training samples and 135 test samples, and we found that all 675 samples could be accurately identified. Finally, superior classification performance was demonstrated by comparing with a traditional machine learning algorithm (genetic algorithm-support vector machine) and a deep learning algorithm (two-dimensional convolutional neural network). The results show that LIF spectroscopy combined with 1D CNN can be used for the fast and accurate identification of mine water inrush without the need for complex pretreatments.

Study of the role of oxygen vacancies as active sites in reduced graphene oxide-modified TiO2.

In recent years, substantial efforts have been devoted to exploring reduced graphene oxide/TiO2 (RGO/TiO2) composite materials; however, there is still a paucity of reports on the construction of reduced graphene oxide/TiO2 with oxygen vacancies (RGO/TiO2-OV) via a facile two-step wet chemistry approach. In this work, we show a proof-of-concept study follow RGO introduced into TiO2 with oxygen vacancies, the role of oxygen vacancies as active sites in reduced graphene oxide-modified TiO2. The photocatalytic performance and related properties of blank-TiO2, blank-TiO2 with oxygen vacancies (blank-TiO2-OV), RGO/TiO2, and RGO/TiO2-OV were comparatively studied. It was found that due to the incorporation of RGO, RGO/TiO2 and RGO/TiO2-OV exhibit a higher photocatalytic performance under simulated solar light irradiation than their counterparts without rGO. More importantly, it was found that blank-TiO2 has a higher photocatalytic activity than blank-TiO2-OV under simulated solar light irradiation. However, RGO/TiO2 shows a lower photocatalytic activity than rGO/TiO2-OV. By a series of combined techniques, we found that the introduction of a component, such as RGO, with the matched energy band to TiO2 could lead to the formation of a long-lived electron-transfer state, thus prolonging the lifetime of the photogenerated charge carriers. Furthermore, during the photocatalytic process, RGO could tune the role of oxygen vacancies in TiO2 from recombination centers to active sites. These findings are of great significance for the design of effective photocatalytic materials in the field of solar energy conversion.