Rapid and accurate fault diagnosis of smart meters can greatly improve the operational and maintenance ability of power systems. Focusing on the historical fault data information of smart meters, a fault diagnosis model of smart meters based on an improved capsule network (CapsNet) is proposed. First, we count the sample size of each fault type, and a mixed sampling method combining undersampling and oversampling is used to solve the problem of distribution imbalance of sample size. The one-hot encoding method is adopted to solve the problem of the fault samples containing more discrete and disordered data. Then, the strong adaptive feature extraction capability and nonlinear mapping capability of the deep belief network (DBN) are utilized to improve the single convolution layer feature extraction part of a traditional capsule network; DBN can also address the problem of high data dimensions and sparse data due to one-hot encoding. The important features and key information of the input sample are extracted and used as the input of the primary capsule layer, and the dynamic routing algorithm is used to construct the digital capsule. Finally, the results of experiments show that the improved capsule network model can effectively improve the accuracy of diagnosis and shorten the training time.

Herein, a PbSe quantum dot-doped-mode-locked fiber laser is experimentally demonstrated. A PbSe quantum dot-doped fiber is prepared using a melting method and induced as a gain medium in our mode-locked fiber laser. By increasing the pump power, a stable pulse train is obtained with a pulse duration of 36 ps, a pulse repetition rate of 4.5 MHz, an average laser power of 9.8 mW, and a central wavelength of 1214.5 nm. The pulse duration can be changed by adjusting the PC or increasing the pump power. The maximum laser power obtained was 42.7 mW under the pump power of 800 mW. Our results prove that a quantum dot-doped-mode-locked fiber laser is achievable, which provides a new scheme to solve wavelength problem of rare-earth-doped mode-locked fiber lasers.

Rule-learning extracts the knowledge from a dataset and represent it in a form that is easy for people to understand. RIPPER (Repeated Incremental Pruning to Produce Error Reduction) and PART (Partial Decision Trees) are two well-known schemes for ru