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This paper addresses a challenging problem -how to generate multi-view cloth images from only a single view input. To generate realistic-looking images with different views from the input, we propose a new image generation model termed VariGANs that combines the strengths of the variational inference and the Generative Adversarial Networks (GANs). Our proposed VariGANs model generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts. It first performs variational inference to model global appearance of the object (e.g., shape and color) and produce a coarse image with a different view. Conditioned on the generated low resolution images, it then proceeds to perform adversarial learning to fill details and generate images of consistent details with the input. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that images of a novel view generated by our model are more plausible than those generated by existing approaches, in terms of more consistent global appearance as well as richer and sharper details.

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Partial discharge (PD) measurements are an important tool for assessing the health of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system consisting of a radio frequency current transducer (RFCT) sensor, a digital storage oscilloscope and a high performance personal computer to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power system apparatus. Wavelet analysis was applied to pre-process measurement data obtained from the wide bandwidth PD sensor. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments where the trained SVM was tested using measurement data from the RFCT as opposed to conventional measurement data indicate that this approach has a robust performance and has great potential for use with field measurement data.

In this paper, we propose a two-stream transformer networks (TSTN) approach for video-based face alignment. Unlike conventional image-based face alignment approaches which cannot explicitly model the temporal dependency in videos and motivated by the fact that consistent movements of facial landmarks usually occur across consecutive frames, our TSTN aims to capture the complementary information of both the spatial appearance on still frames and the temporal consistency information across frames. To achieve this, we develop a two-stream architecture, which decomposes the video-based face alignment into spatial and temporal streams accordingly. Specifically, the spatial stream aims to transform the facial image to the landmark positions by preserving the holistic facial shape structure. Accordingly, the temporal stream encodes the video input as active appearance codes, where the temporal consistency information across frames is captured to help shape refinements. Experimental results on the benchmarking video-based face alignment datasets show very competitive performance of our method in comparisons to the state-of-the-arts.

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Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs) is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.Energies 2016, 9, 778 2 of 20 from among massive sets of data, have been used to predict the transient stability [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The transient stability prediction can be treated as a two-class classification (stable and unstable) problem and solved by machine learning methods. A set of appropriate features/attributes is selected to generate the offline training sets, then an appropriate classification method is utilized to predict the transient stability status.There are two kinds of machine learning-based methods with different types of inputs [2]. One uses pre-fault steady-state variables as the original data, the machine learning method will be used to build the mapping between the steady-stable variables and the transient stability status with respect to an anticipated but not yet occurred contingency [2][3][4][5]. Once the current status is identified as insecure, preventive control can be carried out to modify the system to a secure state. However, when a serious fault happens, or failure of primary relay protection, the power system may still be unstable even if the preventive control has been conducted. Therefore we should emphasize importance of the study on transient stability prediction using post-fault responses. Because the post-fault responses carry information about the influence of the faults on the power system, the prediction is independent of the faults. This arises the second way of transient stability prediction based on machine learning techniques.With the development of wide-area measurement systems, the dynamic response of power systems...

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