Chuankai Yang

Proteins undergo a series of conformational changes when affected by the applied electric field, which changes their functions and properties. The conformational changes in proteins in various electric fields are different due to their internal structures. This study simulates the molecular dynamics of proteins in different amounts and directions of electric fields with gromacs software. According to the root mean square deviation, hydrogen bond, dipole moment, and solvent accessible surface area, it is proved that the conformation change in proteins is more drastic under the simultaneous action of multiple electric fields under various directions, and different fragments unfold with divergent electric fields combined, which is of great importance to control protein function, improve biochemical research and production efficiency in the food and drug safety field.

During transformer operations, production of particles in transformer oil is inevitable, which might jeopardize the transformer’s ability for operation safely. Therefore, identifying pollutants in transformer oil is crucial. Here, we propose a recognition technique that combines lens-free holography with deep learning based on object detection algorithms. First, we capture the impurity image of transformer oil with a lens-free device and then reconstruct the image to enhance the feature extraction efficiency of the convolutional network. We validated this concept through experiments and trained the reconstructed impurity image of transformer oil using the YOLOv5 and Faster-RCNN networks. Between these two network models, the YOLOv5 model showed better overall performance. Both networks improved the recognition performance of the reconstructed images, with YOLOv5 achieving the highest detection performance, with an F1 value exceeding 73%. This technology can successfully identify and mark various pollutants in transformer oil. Moreover, the experimental device is simple and can detect a large number of holograms of transformer oil samples in a short time. The average detection time for each image is only 15ms. This quality detection method for transformer oil samples can be extended to other small particle detection applications.

Cellulose particles are among the aging products of the insulating paper that are used in power transformers. Too many cellulose particles can cause transformer accidents. Traditional research and detection methods that are used for this problem generally focus on the number and length information of cellulose particles, and it is usually difficult to quantitatively describe the spatial shape of cellulose particles. However, the shape of cellulose particles is also one of the factors affecting the safety of transformer insulation. In this paper, we successfully extracted quantitative information of the spatial shape of cellulose particles in transformer oil using an image processing technique and the transport of intensity equation, providing a new novel approach for the study and detection of the shape of cellulose particles in transformer oil.