Aiming to address the significant deviations in transformer fault identification when uneven fault data is processed, a hybrid integration model based on the Improved Light Gradient Boosting Machine (GCLightGBM) is proposed in the present study for effective transformer fault identification. This model consists of two key components. On the one hand, an improved LightGBM model called GCLightGBM is introduced. On the other hand, a hybrid integration model based on GCLightGBM is developed to resolve the problem that the recognition ability of the model is affected by parameter specificity values in GCLightGBM. GCLightGBM combines a gradient harmonized loss function with a cross-entropy loss function, which makes the model better focus on a few critical samples in the dataset. By doing so, GCLightGBM improves the performance of the model in identifying faults accurately. The hybrid integration model model aims to further improve accuracy while ensuring that the model maintains a high performance on realistic variable unbalanced datasets. This approach improves the accuracy of fault identification while still rendering the dataset imbalanced. The experimental results demonstrate that GCLightGBM is effective in solving the low accuracy for certain classes of samples. In addition, the validity of the fault identification method based on the GCLightGBM hybrid integral model is validated on a publicly available dataset, making the model suitable for transformer fault identification.

The accuracy of the high-precision ring laser gyroscopes is influenced by various factors in the external multi-physics environment, including temperature, mechanical, and magnetic fields. Among them, complex temperature field changes lead to degradation of gyroscope accuracy, limiting application scope. This paper proposes a ring laser gyroscopes bias compensation method based on Gaussian process regression and multi-temperature point measurement configuration. We conduct a laser gyroscope thermal analysis based on internal structure, construct a multi-temperature point measurement configuration that effectively reflects the overall temperature characteristics of the ring laser gyroscope. To solve the non-linear multi-dimensional parameter regression problem in the temperature compensation model, we introduce the Gaussian process regression model for temperature compensation. Validating the proposed method through multiple temperature experiments, the results show it outperforms traditional linear regression models and neural network models for temperature compensation.

In order to better observe the defects in the laser scattering image on the surface of the ultra-smooth optical substrate, the volume scattering noise needs to be deducted. This paper attempts to use three methods, which are wavelet denoising, wavelet enhancement and gradient model construction, and compares the advantages and disadvantages of the three methods.