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41.
基于关联规则的分布式通信网告警相关性研究 总被引:3,自引:0,他引:3
描述了基于数据挖掘的通信网告警相关性分析.在分布式数据库中直接运用序列算法效率很低,因为这需要大量的额外通信.为此提出了一种有效的分布式关联规则挖掘算法--EDMA,它通过局部剪枝与全局剪枝来最小化候选项集数目和通信量.在局部站点上运用先进的压缩关联矩阵CMatrix统计局部项集支持数.此外还利用项目剪枝与交易剪枝共同来减少扫描时间.最后仿真验证了EDMA比其他经典分布式算法有更高的运算效率、更低的通信开销以及更好的可扩展性. 相似文献
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电子编务在现代化科技期刊编辑出版中的应用 总被引:1,自引:0,他引:1
通过对电子编务在现代化科技期刊编辑出版中的应用探讨,使我们进一步认识电子编务不仅可以提升编辑在日常编辑工作的效率,使得编辑出版流程环节简化、规范,强化稿件管理,提高出版效率,而且对编辑部在竞争激烈的科技期刊出版环境下生存和发展能力的提高起着重要的作用。 相似文献
46.
An introduction to TMN 总被引:1,自引:0,他引:1
J. Kirk Shrewsbury 《Journal of Network and Systems Management》1995,3(1):13-38
The Telecommunication Management Network standard (ITU-T M.3010), the Network Magagement Forum OMNIPoint program, and many related developments are summarized. TMN and OMNIPoint are presented as a means to achieve integrated network management in the telecommunications industry by calling for all management systems to include an interoperable interface which permits each system to be integrated into a larger management hierarchy. By means of such interfaces, hierarchical integration of management systems can be achieved within the administration of a single service provider. Interoperable interfaces also make possible the interconnection of multiple service providers and the connection of customer to service provider, resulting in the possibility of industry wide integration of management infrastructures at all levels. 相似文献
47.
“计算机人才资源信息系统”是新兴学科领域内的一门新技术。主要将管理,数据库,报表,汉卡,图像,图形,输出排版等浑于一体。并 在图形处理技术与计算机网络技术相结合的过程中,实现图形计算机网络,使图形,图像,数据等资源共享。该系统具有很高的实用价值和良好的应用推广前景。 相似文献
48.
Deep Neural Network (DNN), one of the most powerful machine learning algorithms, is increasingly leveraged to overcome the bottleneck of effectively exploring and analyzing massive data to boost advanced scientific development. It is not a surprise that cloud computing providers offer the cloud-based DNN as an out-of-the-box service. Though there are some benefits from the cloud-based DNN, the interaction mechanism among two or multiple entities in the cloud inevitably induces new privacy risks. This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services. We systematically and thoroughly review privacy attacks and defenses in the pipeline of cloud-based DNN service, i.e., data manipulation, training, and prediction. In particular, a new theory, called cloud-based ML privacy game, is extracted from the recently published literature to provide a deep understanding of state-of-the-art research. Finally, the challenges and future work are presented to help researchers to continue to push forward the competitions between privacy attackers and defenders. 相似文献
49.
As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified. 相似文献
50.
《The Electricity Journal》2022,35(5):107130
Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value. 相似文献