Figure 1: Comparison between our WAS-VTON and recent state-of-the-art methods. With the searched architecture specifically designed for the try-on task, WAS-VTON can perform high-quality full-body virtual try-on and achieves better results than previous architecture-fixed approaches.

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Power transformers are important pieces of equipment for the operation of power systems. Accurate diagnosis of their fault is closely related to the stable operation of the entire power grid. In order to improve the diagnostic accuracy of transformer fault, the grey wolf optimization (GWO) algorithm is introduced, and the differential evolution mechanism is integrated into the algorithm. Therefore, this paper proposes a transformer fault diagnosis method based on the modified grey wolf optimization algorithm (MGWO) and support vector machine (SVM), so that the application method realizes optimization of the penalty factor and the kernel parameter in SVM. Through the analysis of existing data examples, the SVM model optimized by the MGWO algorithm has the advantages of good generalization and strong predictive ability, and its fault diagnostic accuracy is higher than those of the genetic algorithm, particle swarm optimization algorithm, and GWO algorithm. This method has practical application significance.

A new method for fault location of a power distribution network based on Improved Cuckoo Search Algorithm is proposed. Cuckoo Search Algorithm uses Lévy flight to simulate the global parasitic propagation mechanism of the cuckoo population. Therefore, the algorithm avoids falling into local optimum easily. According to the requirements of quickness and accuracy for fault location, the algorithm is improved by combining the search step size of the algorithm and the number of iterations. It improves the algorithm's early iteration speed and subsequent search accuracy. In this paper, the improved Cuckoo Search Algorithm is used to generate random status for all line segments. Then, convert it to the expected status for each switch. And next, update the line segment status by iteration of the algorithm, which makes the expected status of the switches approach to status uploaded by the FTU. Finally, the model outputs the fault location. In this way, a new generic switching function is proposed. It is suitable for single or multiple faults under single and multiple power conditions, which greatly extends the range of applications and versatility. In the evaluation function, the anti-false positive factor and the assumed fault number are introduced. Both of them improve effectiveness and adaptability. And, the validity of these ideas was proved by simulation. Compared with Cuckoo Search Algorithm, Binary Particle Swarm Optimization Algorithm and Genetic Algorithm, Improved Cuckoo Search Algorithm turns out to be good at finding an optimal solution to multiple faults location problems with a faster convergence speed and higher accuracy.

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