Biblio

The output of rolling bearings, as one of the most widely used support elements, has a significant impact on the equipment's stability and protection. Automatic and effective mining of features representing performance condition plays an important role in ensuring its reliability. However, in the actual process, there are often differences in the quality of features extracted from feature engineering, and this difference cannot be evaluated by commonly used methods, such as correlation metric and monotonicity metric. In order to accurately and automatically evaluate and select effective features, a novel assessment metric is established based on the attributes of the feature itself. Firstly, the features are extracted from different domains, which contain differential information, and a feature set is constructed. Secondly, the performances of the features are evaluated and selected based on internal distance and external distance, which is a novel feature evaluation model for classification task. Finally, an adaptive boosting strategy that combines multiple weak learners is adopted to achieve the fault identification at different severities and orientations. One experimental bearing dataset is adopted to analyze, and effectiveness and accuracy of proposed metric index is verified.

To ensure the safe operation of power system, this paper studies two technologies of data encryption and digital signature, and designs a real-time data encryption system based on DES algorithm, which improves the security of data network communication. The real-time data encryption system of power system is optimized by the hybrid encryption system based on DES algorithm. The real-time data encryption of power system adopts triple DES algorithm, and double DES encryption algorithm of RSA algorithm to ensure the security of triple DES encryption key, which solves the problem of real-time data encryption management of power system. Java security packages are used to implement digital signatures that guarantee data integrity and non-repudiation. Experimental results show that the data encryption system is safe and effective.

Smart grid is the key infrastructure of the country, and its network security is an important link to ensure the national important infrastructure security. SOC as a secure operation mechanism for adaptive and continuous improvement of information security, it is practically significant to address the challenge to the network security of the smart grid. Based on the analysis of the technical characteristics and security of smart grid, and taking a grid enterprise smart grid as an example, we propose the design scheme and implementation plan of smart grid SOC platform. Experimental results show that the platform we designed can meet the performance requirements, it also meets the requirements of real-time storage of behavioral data and provides support for interactive analysis and batch analysis.

How to generate multi-view images with realistic-looking appearance from only a single view input is a challenging problem. In this paper, we attack this problem by proposing a novel image generation model termed VariGANs, which combines the merits of the variational inference and the Generative Adversarial Networks (GANs). It 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 produces coarse images of different views. Conditioned on the generated coarse images, it then performs adversarial learning to fill details consistent with the input and generate the fine images. Extensive experiments conducted on two clothing datasets, MVC and DeepFashion, have demonstrated that the generated images with the proposed VariGANs are more plausible than those generated by existing approaches, which provide more consistent global appearance as well as richer and sharper details.

In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.