Direct reconstruction of the effective atomic number of materials by the method of multi-energy radiography

A direct method is proposed for reconstruction of the effective atomic number by means of multi-energy radiography of the material. The accuracy of the method is up to 95% . Advantages over conventional radiographic methods, which ensure accuracy of just about 50%, are discussed. A physical model has been constructed, and general expressions have been obtained for description of the effective atomic number in a two-energy monitoring scheme. A universal dependence has been predicted for the effective atomic number as a function of relative (two-energy) radiographic reflex. The established theoretical law is confirmed by the experimental data presented. The proposed development can find multiple applications in non-destructive testing and related fields, including those in the civil sphere as well as anti-terrorist activities.Comment: 15 pages LaTeX, 4 figures, the paper accepted in Nuclear Methods and Instruments in Physics Research, Section

Detection of organic materials by spectrometric radiography method

In this paper we report a spectrometric approach to dual-energy digital radiography that has been developed and applied to identify specific organic substances and discern small differences in their effective atomic number. An experimental setup has been designed, and a theoretical description proposed based on the experimental results obtained. The proposed method is based on application of special reference samples made of materials with different effective atomic number and thickness, parameters known to affect X-ray attenuation in the low-energy range. The results obtained can be used in the development of a new generation of multi-energy customs or medical X-ray scanners.Comment: 6 pages, 2 tables, 5 figures, will be presented at the Workshop on X-Ray Imaging, 22-24 October, 2008, Dresden, German

Ontology-based access to temporal data with Ontop: a framework proposal

Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop