In order to preserve the spatial and spectral information of the original panchromatic and multi-spectral images, this study designs a loss function suitable for pan-sharpening and a four-layer convolution neural network that could adequately extract spectral and spatial features from original source images. The major advantage of this study is that the designed loss function does not need the reference fused image, and then the proposed pan-sharpening method does not need to make the simulation data for training. This is the big difference from most existing pan-sharpening methods. Moreover, the loss function takes into account the characteristics of remote sensing images, including the spatial and spectral evaluation indicators. We also add the feature enhancement layer in convolution neural network, thus, the proposed four-layer network contains feature extraction, feature enhancement, linear mapping and reconstruction. In order to evaluate the effectiveness and universality of the proposed fusion model, we selected thousands of remote sensing images which include different sensors, different times and different land-cover types to make the training dataset. By evaluating the performance on the WorldView-2, Pleiades and Gaofen-1 experimental data, the results show that the proposed method achieves optimal performance in terms of both the subjective visual effect and the object assessment.The code is available at https://github.com/Zhangxi-Xiong/pan-sharpening

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

During the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition) and correlation dimension and a classification method with BP (back propagation) neural network. Firstly, original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, correlation dimension of the top four IMFs by the G–P algorithm is calculated and the characteristic vector of the vibration signal of the circuit breaker is formed. At last, the classification of characteristic parameter is realized with a simple BP neural network for fault diagnosis. The experimentation without loads indicates that the method can easily and accurately diagnose breaker faults and exploit a new road for diagnosis of high-voltage circuit breakers.