New method of reliability analysis and calculation on grey maintainable system
A new model for reliability analysis and calculation of the grey maintainable system is put forward based on the grey theory to tackle the problem of lacking information for practical application. The feasibility of the proposed method is confirmed by a numerical example. The feature of this new method is that it can analyze system reliability dynamically and master its development law because it does not depend on the distribution parameters of system. Hence, it can be used to preview the reliability of system.
On air defense communications network connectivity
The authors address the issue of communication network survivability and provide a quantitative measure of the time-varying connectivity of space and ground multimedia networks operating in stressed environments. This measure allows comparison of very different network architectures in terms of connectivity. A generic SBR (Space-Based Radar) constellation was selected for study of the communications connectivity. The SBR, consisting of constellations of low Earth orbit satellites, will require dynamic, robust communication protocols and networks to support the various missions for the Air Force, Navy, and other military organizations. The authors present a methodology that can be used to define connectivity requirements once the threat and system parameters are better defined.<<ETX>>
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.