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.

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.

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.

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.

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

In recent years, substantial efforts have been devoted to exploring reduced graphene oxide/TiO (RGO/TiO) composite materials; however, there is still a paucity of reports on the construction of reduced graphene oxide/TiO with oxygen vacancies (RGO/TiO-OV) via a facile two-step wet chemistry approach. In this work, we show a proof-of-concept study follow RGO introduced into TiO with oxygen vacancies, the role of oxygen vacancies as active sites in reduced graphene oxide-modified TiO. The photocatalytic performance and related properties of blank-TiO, blank-TiO with oxygen vacancies (blank-TiO-OV), RGO/TiO, and RGO/TiO-OV were comparatively studied.