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Digitale Medien

Uptake and metabolism of glutamine in cultured kidney cells

Dass, P.D. ; Wu, M.-c.

Amsterdam : Elsevier

Biochimica et Biophysica Acta (BBA)/Molecular Cell Research 845 (1985), S. 94-100

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ISSN: 0167-4889

Schlagwort(e): (Kidney cell) ; Amino acid metabolism ; Glutamine uptake

Quelle: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002

Thema: Biologie , Chemie und Pharmazie , Medizin , Physik

Materialart: Digitale Medien

URL: http://linkinghub.elsevier.com/retrieve/pii/0167-4889(85)90059-X

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Digitale Medien

An efficient method for creating benchmark classifications for automatic workpiece classification systems (1998)

Hsu, S. H. ; Hsia, T. C. ; Wu, M. C.

Springer

The international journal of advanced manufacturing technology 14 (1998), S. 481-494

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ISSN: 1433-3015

Schlagwort(e): Automatic workpiece classification system ; Classification benchmarks ; Full-data classification ; Lean classification

Quelle: Springer Online Journal Archives 1860-2000

Thema: Maschinenbau

Notizen: Abstract The usefulness of an automatic workpiece classification system depends primarily on the extent to which its classification results are consistent with users' judgments. Thus, to evaluate the effectiveness of an automatic classification system it is necessary to establish classification benchmarks based on users' judgments. Such benchmarks are typically established by having subjects perform pair comparisons of all workpieces in a set of sample workpieces. The result of such comparisons is called a full-data classification. However, when the number of sample workpieces is very large, such exhaustive comparisons become impractical. This paper proposes a more efficient method, called lean classification, in which data on comparisons between the samples and a small number of typical workpieces are used to infer the complete classification results. The proposed method has been verified by using a small set of 36 sample workpieces and by computer simulation with medium to large sets of 100 to 800 sample workpieces. The results reveal that the method could produce a classification that was 71% consistent with the full-data classification while using only 10% of the total data.