The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Despite their high specific stiffness and strength, carbon fiber reinforced polymers, stacked at different fiber orientations, are susceptible to interlaminar damages. They may occur in the form of micro-cracks and voids, and leads to a loss of performance. Within this framework, ultrasonic tests can be exploited in order to detect and classify the kind of defect. The main object of this work is to...
One of the major issues in wireless sensor network applications is the way how to minimize energy consumption in the process of data transfer. Data in wireless sensor networks should be aggregated in order to save energy and bandwidth. There are lots of works on the area of fault-tolerant and efficient data aggregation for WSNs in different approach. In this paper an efficient fault tolerant data...
Ontology alignment is a time consuming process, especially when the two ontologies to be aligned are large. A fast and accurate ontology similarity can help the user to avoid aligning ontologies without significant similarities. In this paper, we propose an Asymmetric Similarity Measure for Ontologies (ASMO) that measures how similar the source ontology is to the target ontology. Many efficient ontology...
Failure detection is a fundamental building block for ensuring fault tolerance in large scale distributed systems. In this paper we present an innovative solution to this problem. The approach is based on adaptive, decentralized failure detectors, capable of working asynchronous and independent on the application flow. The proposed failure detectors are based on clustering, the use of a gossip-based...
K-means Clustering is an important algorithm for identifying the structure in data. K-means is the simplest clustering algorithm. This algorithm uses predefined number of clusters as input. The original algorithm is based on random selection of cluster centers and iteratively improving the results. However there are two major limitations in this approach. First, the need for number of clusters in...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.