Circuit Breaker Fault Diagnosis Method Based on Improved One-Dimensional Convolutional Neural Network

Aiming at the problems of manual feature extraction and poor generalization ability of model in traditional circuit breaker fault diagnosis technology, a circuit breaker fault diagnosis method based on improved one-dimensional convolutional neural network is proposed. Firstly, the input feature sequence is adaptively weighted by self-attention mechanism to highlight the weight of important information; Secondly, 1 1 convolution layer and global average pooling layer are used to replace the full connection layer, which reduces the model training parameters, improves the training efficiency and prevents the phenomenon of over-fitting. Aiming at the problem of small number of data samples, the data is enhanced by Generative Adversarial Network. After adding the generated data to the original data, the accuracy of fault identification is further improved. The experimental results show that this method can effectively and accurately identify different fault types of circuit breaker, and verify the feasibility of its engineering application

Future Climate Change Will Have a Positive Effect on Populus Davidiana in China

Since climate change significantly affects global biodiversity, a reasonable assessment of the vulnerability of species in response to climate change is crucial for conservation. Most existing methods estimate the impact of climate change on the vulnerability of species by projecting the change of a species’ distribution range. This single-component evaluation ignores the impact of other components on vulnerability. In this study, Populus davidiana (David’s aspen), a tree species widely used in afforestation projects, was selected as the research subject under four future climate change scenarios (representative concentration pathway (RCP)2.6, RCP4.5, RCP6.0, and RCP8.5). Exposure components of range change as well as the degree of fragmentation, degree of human disturbance, and degree of protection were considered simultaneously. Then, a multicomponent vulnerability index was established to assess the effect of future climate change on the vulnerability of P. davidiana in China. The results show that the distribution range of P. davidiana will expand to the northwest of China under future climate change scenarios, which will lead to an increased degree of protection and a decreased degree of human disturbance, and hardly any change in the degree of fragmentation. The multicomponent vulnerability index values of P. davidiana under the four emission scenarios are all positive by 2070, ranging from 14.05 to 38.18, which fully indicates that future climate change will be conducive to the survival of P. davidiana. This study provides a reference for the development of conservation strategies for the species as well as a methodological case study for multicomponent assessment of species vulnerability to future climate change