• Progress in Medical Physics
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• v.13 no.3
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• pp.149-155
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• 2002

Recent advances in radiation transport algorithms, computer hardware performance, and parallel computing make the clinical use of Monte Carlo based dose calculations possible. To compare the speed and accuracies of dose calculations between different developed codes, a benchmark tests were proposed at the XIIth ICCR (International Conference on the use of Computers in Radiation Therapy, Heidelberg, Germany 2000). A Monte Carlo treatment planning comprised of 28 various Intel Pentium CPUs was implemented for routine clinical use. The purpose of this study was to evaluate the performance of our system using the above benchmark tests. The benchmark procedures are comprised of three parts. a) speed of photon beams dose calculation inside a given phantom of 30.5 cm$\times$39.5 cm $\times$ 30 cm deep and filled with 5 ㎣ voxels within 2% statistical uncertainty. b) speed of electron beams dose calculation inside the same phantom as that of the photon beams. c) accuracy of photon and electron beam calculation inside heterogeneous slab phantom compared with the reference results of EGS4/PRESTA calculation. As results of the speed benchmark tests, it took 5.5 minutes to achieve less than 2% statistical uncertainty for 18 MV photon beams. Though the net calculation for electron beams was an order of faster than the photon beam, the overall calculation time was similar to that of photon beam case due to the overhead time to maintain parallel processing. Since our Monte Carlo code is EGSnrc, which is an improved version of EGS4, the accuracy tests of our system showed, as expected, very good agreement with the reference data. In conclusion, our Monte Carlo treatment planning system shows clinically meaningful results. Though other more efficient codes are developed such like MCDOSE and VMC＋＋, BEAMnrc based on EGSnrc code system may be used for routine clinical Monte Carlo treatment planning in conjunction with clustering technique.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.63-77
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• 2011

The World Wide Web is a very large distributed digital information space. From its origins in 1991, the web has grown to encompass diverse information resources as personal home pasges, online digital libraries and virtual museums. Some estimates suggest that the web currently includes over 500 billion pages in the deep web. The ability to search and retrieve information from the web efficiently and effectively is an enabling technology for realizing its full potential. With powerful workstations and parallel processing technology, efficiency is not a bottleneck. In fact, some existing search tools sift through gigabyte.syze precompiled web indexes in a fraction of a second. But retrieval effectiveness is a different matter. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Furthermore, the most relevant documents do not nessarily appear at the top of the query output order. Also, current search tools can not retrieve the documents related with retrieved document from gigantic amount of documents. The most important problem for lots of current searching systems is to increase the quality of search. It means to provide related documents or decrease the number of unrelated documents as low as possible in the results of search. For this problem, CiteSeer proposed the ACI (Autonomous Citation Indexing) of the articles on the World Wide Web. A "citation index" indexes the links between articles that researchers make when they cite other articles. Citation indexes are very useful for a number of purposes, including literature search and analysis of the academic literature. For details of this work, references contained in academic articles are used to give credit to previous work in the literature and provide a link between the "citing" and "cited" articles. A citation index indexes the citations that an article makes, linking the articleswith the cited works. Citation indexes were originally designed mainly for information retrieval. The citation links allow navigating the literature in unique ways. Papers can be located independent of language, and words in thetitle, keywords or document. A citation index allows navigation backward in time (the list of cited articles) and forwardin time (which subsequent articles cite the current article?) But CiteSeer can not indexes the links between articles that researchers doesn't make. Because it indexes the links between articles that only researchers make when they cite other articles. Also, CiteSeer is not easy to scalability. Because CiteSeer can not indexes the links between articles that researchers doesn't make. All these problems make us orient for designing more effective search system. This paper shows a method that extracts subject and predicate per each sentence in documents. A document will be changed into the tabular form that extracted predicate checked value of possible subject and object. We make a hierarchical graph of a document using the table and then integrate graphs of documents. The graph of entire documents calculates the area of document as compared with integrated documents. We mark relation among the documents as compared with the area of documents. Also it proposes a method for structural integration of documents that retrieves documents from the graph. It makes that the user can find information easier. We compared the performance of the proposed approaches with lucene search engine using the formulas for ranking. As a result, the F.measure is about 60% and it is better as about 15%.