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Based on the equipment problems of capacitor voltage transformer (CVT) caused by capacitor element breakdown and the analyzed situation towards these equipments, this research made tests on the CVT and calculated the tested data, capacitor element numbers, and capacitance theoretically, and finally put up with the fluctuation value range for detecting the secondary voltage of CVT and the strategies for operation and maintenance, which will improve the problem-finding probability of CVT by detecting secondary voltage.

Recommendation for new users is a key challenge due to the lack of prior information from them, which is the well-known cold-start problem. Preference elicitation has been proposed as an efficient strategy for eliciting new users preference through an initial interview where new users are queried by elaborately selected items. In this paper, we propose a novel community discovering guided discriminative selection (CDDS) model for constructing query set. We exploit the community as an effective information which is not fully used in existing approaches. By integrating item selection and community discovery into one framework, our model selects most discriminative items for preference elicitation, with guidance of unsupervised community discovering process. To perform community discovering process, the model utilizes rating similarity graph and social network as a graph regular-ization. Experimental results on real-world datasets Flixster and Douban demonstrate that the proposed method outperforms traditional preference elicitation methods for cold-start recommendation.

Augmenting the semantic attribute representation with the discriminative features has been proved to be an effective method for improving the performance of object classification. However, how to make the expanded features more effective and discriminative is still an open problem. In this paper, we propose a Sequential Augmented features Learning method (SAL) to implement semantic attribute augmentation. In our SAL method, the augmented non-semantic features are learned one by one under a sequential error-correcting scheme so that we can obtain more discriminating power with very compact expanded features. Extensive experiments are conducted on a public dataset and the results show that our approach achieves encouraging performance.

The goal of relevance feedback is to improve the performance of image retrieval by leveraging the labeling of human. It is helpful to introduce active learning method in relevance feedback to alleviate the human burden. In the traditional active learning the samples which can improve the classifier the most if they were labeled are selected for the user's labeling. However, the change of the geometrical structure of the data distribution caused by such expensive labeled samples is not fully exploited. By mining user's labeling information, we can reduce the original feature space dimension to ease the classifier's instability brought by the small sample size. In this paper, we propose a novel batch mode active learning method for informative data selection. The labeled samples are not only used to retrain the classifier, but to learn a subspace which efficiently encodes user's intention as well. Especially, a scheme of certainty propagation on the subspace effectively integrates uncertainty sampling and subspace learning into the proposed Subspace learning based batch mode Active Learning method (SubAL) in relevance feedback. Extensive experiments on publicly available dataset shows that the proposed method is promising.

Relevance feedback is a quite effective approach to improve performance for image retrieval. Recently, active learning method has attracted much attention due to its capability of alleviating the burden of labeling in relevance feedback. However, most of the traditional studies focus on single sample selection in each feedback which needs heavy computational cost in practice. In this paper, we presents a novel batch mode active learning method for informative sample selection. Inspired by graph propagation, we consider the certainty of labels as asymmetric propagation information on graph, and formulate the correlation between labeled samples and unlabeled samples in an united scheme. Extensive experiments on publicly available data sets show that the proposed method is promising.

Selective sampling has been widely used in relevance feedback of image retrieval to alleviate the burden of labeling by selecting the most informative instances for user to label. Traditional sample selection scheme often selects a batch of instances each time and label them simultaneously, which ignores the correlation among instances and results in redundant labeling. In this paper, we propose an improved representative sampling method with certainty propagation to improve the performance of sampling. In our method, two kinds of correlations among instances are explored to reduce the redundancy in sampling. One is the correlation between labeled instances and unlabeled instances. The other is the correlation among unlabeled instances. Extensive experiments show that the proposed method achieve encouraging results.